Wrapper Feature Selection Approach Research Articles

A novel two-stage wrapper feature selection approach based on greedy search for text sentiment classification

Sentiment analysis is a crucial step in obtaining subjective data from online text sources. Nevertheless, the substantial challenge of high dimensionality prevails within text classification. Addressing this, dimension reduction emerges as a valuable approach to enhance the efficacy of classification in the domain of machine learning. The discerning removal of redundant features not only expedites training processes but also bolsters the achievement of accurate classifications. It is worth noting that the effectiveness of distinct feature selection methodologies can be contingent upon the unique attributes inherent in diverse datasets. Within the purview of this investigation, a novel two-stage approach is introduced, characterized by a greedy search-based wrapper feature selection algorithm. The underpinning of this algorithm involves leveraging the outcomes yielded by filter-based feature selection techniques to establish a prioritized sequence for the scrutiny of features within the proposed framework. This strategic sequencing harnesses the cumulative insights from a series of filter-based methodologies, thereby facilitating the curation of feature subsets that underscore pivotal attributes. Nonetheless, it is acknowledged that the greedy selection approach primarily favors features with high-ranking scores, and thus, it may not adequately evaluate the potential of feature combinations that involve low-scoring elements. An extensive experimental inquiry was conducted across widely recognized sentiment analysis datasets to assess the performance of the introduced methodology. The computational findings eloquently demonstrate that the proposed algorithm attains an average accuracy of 96.88% for nine public sentiment datasets and 94.43% for the Humir datasets when coupled with the multinomial Naive Bayes classifier. Furthermore, the experimental outcomes conspicuously establish the superiority of the proposed technique in state-of-the-art studies across the same set of nine sentiment datasets and the Humir datasets.

Efficient traffic-based IoT device identification using a feature selection approach with Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm

Internet of Things (IoT) refers to the various devices connected to the Internet, enabling them to communicate and transmit data with each other. The rapid development of the IoT also brings security and other problems in cyberspace. In this case, device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. However, device identification has some challenges on IoT traffic datasets, such as large-scale and high-dimensional sparse datasets, features prone to security vulnerabilities, and identification of IP and non-IP devices, which affects the performance of classifiers. Feature selection can be seen as an effective data preprocessing technique in IoT device identification, which may improve the performance of classification and reduce the computational complexity of IoT device identification. In this paper, we propose a traffic-based IoT device identification model using a novel wrapper feature selection approach based on an improved and efficient method, which we call Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm (LS2-BHBA). Specifically, four improved factors are employed in LS2-BHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. In addition, a binary mechanism is implemented to enhance the suitability of the proposed LS2-BHBA for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that LS2-BHBA can reduce the number of features to 10%, and the classification accuracy can reach 98%, which outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification.

Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection.

Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works.

A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

Statistics-Guided Accelerated Swarm Feature Selection in Data-Driven Soft Sensors for Hybrid Engine Performance Prediction

The accurate prediction of soft sensors is essential for the development of modern combustion engines to achieve better performance, lower emissions, and reduced fuel consumption. To precisely predict engine performance, i.e., indicated thermal efficiency, volumetric efficiency, and fuel consumption rate of a hybrid engine, in this article, we propose a novel data-driven approach of statistics-guided accelerated swarm feature selection to find the most effective features for engine soft sensors. Differing from the existing filter or wrapper feature selection approaches, this approach uses external measure information to direct velocity updates in the accelerated swarm feature selection. Several filter and wrapper methods are developed and comprehensively compared. The experimental dataset is collected from a BYD 1.5 L gasoline engine. Validated by bench test, the results demonstrate that the proposed approach finds the most effective features and optimal network structure for data-driven performance prediction of the hybrid engine that was studied.

A new hybrid feature selection based on Improved Equilibrium Optimization

Feature selection is one of the most important preprocessing steps for analyzing high-dimensional data in the data mining and machine learning fields. In machine learning problems, Classification accuracy is highly dependent on the selected features of a dataset. The main purpose of feature selection is to remove additional and irrelevant features from the data feature set to increase the performance of algorithms. A novel feature selection model is proposed in this paper. In the first step, the features are scored based on the mRMR feature selection approach, and those with a higher score are selected. In the second step, the Wrapper feature selection approach is used to extract the best features based on the Improved Equilibrium Optimization (IMEO) algorithm. IMEO uses a new operator called Entropy-based to improve the performance of the Equilibrium Optimizer (EO) algorithm and prevents the algorithm from getting trap in local optima by controlling the exploration and exploitation abilities. Also, Levy flight is used to find new solutions in the search space and to improve the exploration phase of the EO algorithm. This mechanism will avoid local optimum by creating many short jumps and sometimes large jumps in the search space. The proposed IMEO algorithm is first tested on 23 benchmark test functions; after ensuring the efficiency of the proposed method, the Binary Improved Equilibrium Optimization (BIMEO) algorithm is defined based on the sigmoid transfer function to solve the feature selection problem. The simulation results of the BIMEO algorithm and state-of-the-art algorithms on 18 standard benchmark datasets show the performance of the proposed model compared to the other algorithms. In addition, the results of experiments on two chemical datasets prove the efficiency of the proposed algorithm, BIMEO, in terms of selective features and classification accuracy.

Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.

Comparison of feature selection methods for mapping soil organic matter in subtropical restored forests

• Filter, wrapper, embedded and ensemble feature selection approaches (FS) were evaluated. • The proposed ensemble FS incorporates ten individual selectors to identify top significant variables. • Ensemble FS produced the highest predictive variable subset for mapping SOM. • XGBoost outperformed RF in mapping SOM in subtropical restored forests. Mapping Soil organic matter (SOM) over a complex forest landscape is challenging due to the difficulty in selecting the most insightful variables from high-dimensional datasets in the recent explosion of geospatial-data. Feature selection (FS) is necessary to reduce data redundancy and noise as well as to achieve more reliable SOM spatial predictions. However, it is still unclear that which is most effective among various FS methods in mapping SOM. Therefore, four types of FS approaches (i.e., filter, wrapper, embedded and ensemble) were adopted to generate optimum variable subsets from an original variable dataset of 60 candidates, respectively, for mapping SOM of restored forest land in a typical subtropical region of southern China. The most used methods for each type of FS approaches were selected in this study, including three filters (Chi-square, InfoGain and pearson correlation analysis), three wrappers (genetic algorithm, simulated annealing algorithm and support vector machine-recursive feature elimination) and three embedded methods (Boruta, random forest (RF) and extreme gradient boosting (XGBoost)), as well as an ensemble method (robust rank aggreg algorithm (RRA)). Meanwhile, the RF and XGBoost models were applied with a 10-fold cross-validation method to compare the relative advantages of the different FS methods in SOM mapping, by utilizing the correlation coefficients R 2 between observed and predicted values and predicting errors of root mean square error (RMSE). The results show that the SOM prediction accuracies with optimized variable subsets generated by the different FS methods are better than those with full variables, yet the improvements of prediction performance are different among the four types of FS approaches. The ensemble method (RRA) is superior to the other three types of approaches with an average RMSE reduction of 9.16% comparing to that without using FS methods, followed by wrapper and embedded methods which obtained the average RMSE reduction by 7.81%, 7.32%, respectively, and the filter methods are the weakest in the RMSE reduction with slight decreases of 4.32%. The XGBoost model achieved a better performance in predicting SOM than the RF model regardless of input variables, and the XGBoost model combined with RRA FS method shows the greatest potential to map SOM in the restored forest land. This study provides a reference for obtaining more parsimonious and robust variable sets from the available big geo-data freely for soil mapping in other areas.

A hybrid ensemble-filter wrapper feature selection approach for medical data classification

Background and objectiveMedical data plays a decisive role in disease diagnosis. The classification accuracy of high-dimensional datasets is often diminished by several redundant and irrelevant features. In this context, feature selection becomes an indispensable process. Feature selection primarily intends to identify a feature subspace which retains the classification accuracy while reducing the high computational cost of learning model as well as eliminating noise. The suitability of an appropriate feature selection approach heavily depends upon the capability of that approach to match the problem framework and to discover the intrinsic patterns within the data. The prime objective of this paper is to develop an ensemble-filter based hybrid feature selection model for disease detection. MethodsIn this paper, a four-step hybrid ensemble feature selection algorithm has been introduced. Firstly, the dataset is partitioned using the cross-validation procedure. Secondly, in the filter step, various filter methods based on weighted scores were ensembled to generate a ranking of features, and thirdly sequential forward selection algorithm is utilized as a wrapper technique to obtain an optimal subset of features. Finally, the resulting optimal subset is processed for subsequent classification tasks. ResultsExperiments have been performed on twenty benchmark medical datasets with different dimensionalities. The performance of the proposed hybrid approach is compared with fourteen state-of-the-art feature selection algorithms on four benchmark classifiers namely, Naïve Bayes, Support Vector Machine with Radial Basis Function, Random Forest and k-Nearest Neighbor. The empirical results clearly demonstrate the superiority of the proposed hybrid methodology over the competing methods with respect to accuracy, sensitivity, specificity, f1-score, area under curve evaluation measures and number of selected features. The statistical analysis of the obtained results shows the outperformance and the competitiveness of the proposed hybrid method with respect to various state-of-the-art algorithms. ConclusionsThis study concludes that the proposed hybrid approach proves to be more effective and reliable feature selection technique in selecting highly discriminative features. The framework can be utilized as a promising tool by both clinicians and researchers in enhancing the classification performance of medical datasets.

A wrapper feature selection approach for efficient modelling of gully erosion susceptibility mapping

Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is key to the development of an efficient management strategy for policymakers. While efforts into susceptibility mapping of natural disasters have grown in recent years, understanding the most relevant predictive causal factors is still a challenge. As the selection of these factors, among many potentially relevant factors, is an important part of the model selection process, we propose a hybrid intelligent approach for the optimal selection of a set of relevant factors based on logistic regression (LR) and genetic algorithms. In order to verify the effectiveness of the proposed approach, this study also identified areas that were highly susceptible to gully erosion using three different classifiers – namely, the LR, support vector machine (SVM) and k-nearest neighbours (k-NN) techniques. We tested the approach in the Yeli Bedrag watershed in north-eastern Golestan province, Iran. The results showed that the elevation, distance to fault, slope and the index of connectivity were the most important causal factors affecting the successful prediction of gully occurrence. Comparison of maximum True Skill Statistic values showed that increased model sophistication did not necessarily result in a higher level of model performance. In terms of performance, k-NN was superior to the SVM and LR methods. This method can be effectively used for gully erosion susceptibility (GES) zonation in the study area, which is very important to support spatial planning to initiate designing mitigation strategies and conservation needs over a large area, or to plan additional conservation efforts and relocate soil conservation plans. In conclusion, our findings indicate that by incorporating the proposed hybrid intelligent approach, the number of relevant factors for GES mapping was reduced, while classification accuracy was increased.

Prediction of Freezing of Gait in Parkinson's Disease Using Wearables and Machine Learning.

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness.

A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology

Feature selection plays a key role in data mining and machine learning algorithms to reduce the processing time and increase the accuracy of classification of high dimensional datasets. One of the most common feature selection methods is the wrapper method that works on the feature set to reduce the number of features while improving the accuracy of the classification. In this paper, two different wrapper feature selection approaches are proposed based on Farmland Fertility Algorithm (FFA). Two binary versions of the FFA algorithm are proposed, denoted as BFFAS and BFFAG. The first version is based on the sigmoid function. In the second version, new operators called Binary Global Memory Update (BGMU) and Binary Local Memory Update (BLMU) and a dynamic mutation (DM) operator are used for binarization. Furthermore, the new approach (BFFAG) reduces the three parameters of the base algorithm (FFA) that are dynamically adjusted to maintain exploration and efficiency. Two proposed approaches have been compared with the basic meta-heuristic algorithms used in feature selection on 18 standard datasets. The results show better performance of the proposed approaches compared with the competing methods in terms of objective function value, the average number of selected features, and the classification accuracy. Also, the experiments on the emotion analysis dataset demonstrate the satisfactory results.

Isolation Forest Wrapper Approach for Feature Selection in Software Defect Prediction

Software defect prediction is one of the hot research topics in the software engineering application. The performance of predictor largely depends on the quality of dataset used for learning the predictor. High dimensionality is a noteworthy characteristic of software defect dataset, which has some side-effect on the predictor building using data mining or machine learning algorithm. Feature selection, being an effective measure of dimensionality reduction, uses the optimal feature subset to represent the entire feature space and alleviate the dimensionality curse problem. In this paper, a wrapper feature selection approach applying genetic algorithm as a search strategy to find the optimal feature subset is firstly introduced. Secondly, an improved isolation forest based defect prediction method is proposed. The exploring experiments on 5 real NASA software defect datasets demonstrate the proposed method can improve the defect prediction performance to some extent and proves the positive effect of feature selection in SDP application.

Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.

Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities.

An efficient IoT based smart farming system using machine learning algorithms

This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop productivity and drought for proficient decision support making in IoT based smart farming systems. The crop productivity and drought predictions is very important to the farmers and agriculture’s executives, which greatly help agriculture-affected countries around the world. Drought prediction plays a significant role in drought early warning to mitigate its impacts on crop productivity, drought prediction research aims to enhance our understanding of the physical mechanism of drought and improve predictability skill by taking full advantage of sources of predictability. In this work, an intelligent method based on the blend of a wrapper feature selection approach, and PART classification technique is proposed for crop productivity and drought predicting. Five datasets are used for estimating the proposed method. The results indicated that the projected method is robust, accurate, and precise to classify and predict crop productivity and drought in comparison with the existing techniques. From the results, the proposed method proved to be most accurate in providing drought prediction as well as the productivity of crops like Bajra, Soybean, Jowar, and Sugarcane. The WPART method attains the maximum accuracy compared to the existing supreme standard algorithms, it is obtained up to 92.51%, 96.77%, 98.04%, 96.12%, and 98.15% for the five datasets for drought classification, and crop productivity respectively. Likewise, the proposed method outperforms existing algorithms with precision, sensitivity, and F Score metrics.

GeFeS: A generalized wrapper feature selection approach for optimizing classification performance

In this paper, we propose a generalized wrapper-based feature selection, called GeFeS, which is based on a parallel new intelligent genetic algorithm (GA). The proposed GeFeS works properly under different numerical dataset dimensions and sizes, carefully tries to avoid overfitting and significantly enhances classification accuracy. To make the GA more accurate, robust and intelligent, we have proposed a new operator for features weighting, improved the mutation and crossover operators, and integrated nested cross-validation into the GA process to properly validate the learning model. The k-nearest neighbor (kNN) classifier is utilized to evaluate the goodness of selected features. We have evaluated the efficiency of GeFeS on various datasets selected from the UCI machine learning repository. The performance is compared with state-of-the-art classification and feature selection methods. The results demonstrate that GeFeS can significantly generalize the proposed multi-population intelligent genetic algorithm under different sizes of two-class and multi-class datasets. We have achieved the average classification accuracy of 95.83%, 97.62%, 99.02%, 98.51%, and 94.28% while reducing the number of features from 56 to 28, 34 to 18, 279 to 135, 30 to 16, and 19 to 9 under lung cancer, dermatology, arrhythmia, WDBC, and hepatitis, respectively.

An Improved Dragonfly Optimization Algorithm based Feature Selection in High Dimensional Gene Expression Analysis for Lung Cancer Recognition

A microarray gene expression data is an efficient dataset for analyzing expression of thousands of genes and related disease. The more accurate analysis can be obtained by comparing Gene expression of disease tissues with normal tissues which helps to recognize the type of cancer. The processing of microarray datasets such as feature selection, sampling and classification is highly challenged due to its high dimensionality. Many recent researchers used various feature selection techniques for dimensionality reduction. Dragonfly optimization Algorithm (DA) was a feature selection technique used to reduce the dimensionality of lung cancer gene expression dataset. The dragonflies in DA are flying randomly based on the model developed by using the Levy Flight Mechanism (LFM). Because of huge searching steps, LFM has some drawbacks like interruption of arbitrary flights and overflowing of the search area. In fact, DA lacks an internal resemblance that record past potential solutions that can lead to its premature convergence into local optima. So, in this paper an Improved Dragonfly optimization Algorithm (IDA) is introduced which effectively reduces the dimensionality of the lung cancer gene expression dataset. In IDA, Brownian motion method is used to solve the issues of LFM and pbest and gbest idea of Particle Swarm Optimization (PSO) is used to direct the search method for finding potential candidate solutions to further refine the search space for avoiding premature convergence. The wrapper feature selection approach is followed by IDA to select optimal subset of features. The Random Sub space (RS), Artificial Neural Network (ANN) and Sequential Minimal Optimization (SMO) classifiers are utilized for feature selection of IDA and recognize Lung cancer subtypes. The accuracy of the classifier for selected features of Dragon flies in training instances is used as fitness value of Dragon flies in each iteration. Finally, the experimental results prove the effectiveness of the IDA in terms of accuracy, precision, recall and F-measure.

Augmented whale feature selection for IoT attacks: Structure, analysis and applications

Smart connected appliances expand the boundaries of the conventional Internet into the new Internet of Things (IoT). IoT started to hold a significant role in our life, and in several fields as in transportation, industry, smart homes, and cities. However, one of the critical issues is how to protect IoT environments and prevent intrusions. Attacks detection systems aim to identify malicious patterns and threats that cannot be detected by traditional security countermeasures. In literature, feature selection or dimensionality reduction has been profoundly studied and applied to the design of intrusion detection systems. In this paper, we present a novel wrapper feature selection approach based on augmented Whale Optimization Algorithm (WOA), which adopted in the context of IoT attacks detection and handles the high dimensionality of the problem. In our approach, we introduce the use of both V-shaped and S-shaped transfer functions into WOA and compare the superior variant with other well-known evolutionary optimizers. The experiments are conducted using N-BaIoT dataset; wherein, five datasets were sampled from the original dataset. The dataset represents real IoT traffic, which is drawn from the UCI repository. The experimental results show that WOA based on V-shaped transfer function combined with elitist tournament binarization method is superior over S-shaped transfer function and outperforms other well-regarded evolutionary optimizers based on the obtained average accuracy, fitness, number of features, running time and convergence curves. Hence, we can conclude that the proposed approach can be deployed in IoT intrusion detection systems.

A NSGA2-LR wrapper approach for feature selection in network intrusion detection

Feature selection is becoming a major preprocessing phase in which irrelevant and redundant features are removed, while the more informative ones are retained. The datasets used in intrusion detection systems contain many features. It is, therefore, necessary to apply a feature selection step to improve the classification performance and reduce the computation time. In this paper, we propose a multi-objective feature selection approach based on NSGA-II and logistic regression in network intrusion detection. The proposed wrapper approach is tested according to two schemes: the first uses binomial logistic regression with many binary-class datasets corresponding to each type of attack, and the second uses multinomial logistic regression with a multi-class dataset. The best obtained subsets are tested using three different decision tree classifiers namely C4.5 decision tree, Random Forest, and Naive Bayes Tree. Three datasets are used in experiments namely NSL-KDD dataset, UNSW-NB15 dataset, and CIC-IDS2017. The obtained results show better accuracy when using binary-class datasets compared to multi-class datasets. Furthermore, the two schemes for feature selection succeed in reducing the features space by removing irrelevant features and keeping only the most informative ones. The obtained results are promising compared to other approaches.