Traditional Feature Selection Techniques Research Articles

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. Traditional techniques may also struggle with combinatorial optimization problems when applied to a prominent feature space. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quantum Computing based Quadratic unconstrained binary optimization (QC-FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

Towards explainable artificial intelligence through expert-augmented supervised feature selection

This paper presents a comprehensive framework for expert-augmented supervised feature selection, addressing pre-processing, in-processing, and post-processing aspects of Explainable Artificial Intelligence (XAI). As part of pre-processing XAI, we introduce the Probabilistic Solution Generator through the Information Fusion (PSGIF) algorithm, leveraging ensemble techniques to enhance the exploration and exploitation capabilities of a Genetic Algorithm (GA). Balancing explainability and prediction accuracy, we formulate two multi-objective optimization models that empower expert(s) to specify a maximum acceptable sacrifice percentage. This approach enhances explainability by reducing the number of selected features and prioritizing those considered more relevant from the domain expert's perspective. This contribution aligns with in-processing XAI, incorporating expert opinions into the feature selection process as a multi-objective problem. Traditional feature selection techniques lack the capability to efficiently search the solution space considering our explainability-focused objective function. To overcome this, we leverage the Genetic Algorithm (GA), a powerful metaheuristic algorithm, optimizing its parameters through Bayesian optimization. For post-processing XAI, we present the Posterior Ensemble Algorithm (PEA), estimating the predictive power of features. PEA enables a nuanced comparison between objective and subjective importance, identifying features as underrated, overrated, or appropriately rated. We evaluate the performance of our proposed GAs on 16 publicly available datasets, focusing on prediction accuracy in a single objective setting. Moreover, we test our multi-objective model on a classification dataset to show the applicability and effectiveness of our framework. Overall, this paper provides a holistic and nuanced approach to explainable feature selection, offering decision-makers a comprehensive understanding of feature importance.

Text-mining-based feature selection for anticancer drug response prediction.

Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. https://github.com/merlab/text_features.

Optimization of drug-target affinity prediction methods through feature processing schemes.

Numerous high-accuracy drug-target affinity (DTA) prediction models, whose performance is heavily reliant on the drug and target feature information, are developed at the expense of complexity and interpretability. Feature extraction and optimization constitute a critical step that significantly influences the enhancement of model performance, robustness, and interpretability. Many existing studies aim to comprehensively characterize drugs and targets by extracting features from multiple perspectives; however, this approach has drawbacks: (i) an abundance of redundant or noisy features; and (ii) the feature sets often suffer from high dimensionality. In this study, to obtain a model with high accuracy and strong interpretability, we utilize various traditional and cutting-edge feature selection and dimensionality reduction techniques to process self-associated features and adjacent associated features. These optimized features are then fed into learning to rank to achieve efficient DTA prediction. Extensive experimental results on two commonly used datasets indicate that, among various feature optimization methods, the regression tree-based feature selection method is most beneficial for constructing models with good performance and strong robustness. Then, by utilizing Shapley Additive Explanations values and the incremental feature selection approach, we obtain that the high-quality feature subset consists of the top 150D features and the top 20D features have a breakthrough impact on the DTA prediction. In conclusion, our study thoroughly validates the importance of feature optimization in DTA prediction and serves as inspiration for constructing high-performance and high-interpretable models. https://github.com/RUXIAOQING964914140/FS_DTA.

Binary Anarchic Society Optimization for Feature Selection

"Datasets comprise a collection of features; however, not all of these features may be necessary. Feature selection is the process of identifying the most relevant features while eliminating redundant or irrelevant ones. To be effective, feature selection should improve classification performance while reducing the number of features. Existing algorithms can be adapted and modified into feature selectors. In this study, we introduce the implementation of the Anarchic Society Optimization algorithm, a human-inspired algorithm, as a feature selector. This is the first study that utilizes the binary version of the algorithm for feature selection. The proposed Binary Anarchic Society Algorithm is evaluated on nine datasets and compared to three known algorithms: Binary Genetic Algorithm, Binary Particle Swarm Optimization, and Binary Gray Wolf Optimization. Additionally, four traditional feature selection techniques (Info Gain, Gain Ratio, Chi-square, and ReliefF) are incorporated for performance comparison. Our experiments highlight the competitive nature of the proposed method, suggesting its potential as a valuable addition to existing feature selection techniques."

Unlocking the Potential of Autism Detection: Integrating Traditional Feature Selection and Machine Learning Techniques

The diagnostic process for Autism Spectrum Disorder (ASD) typically involves time-consuming assessments conducted by specialized physicians. To improve the efficiency of ASD screening, intelligent solutions based on machine learning have been proposed in the literature. However, many existing ML models lack the incorporation of medical tests and demographic features, which could potentially enhance their detection capabilities by considering affected features through traditional feature selection approaches. This study aims to address the aforementioned limitation by utilizing a real dataset containing 45 features and 983 patients. To achieve this goal, a two-phase methodology is employed. The first phase involves data preparation, including handling missing data through model-based imputation, normalizing the dataset using the Min-Max method, and selecting relevant features using traditional feature selection approaches based on affected features. In the second phase, seven ML classification techniques recommended by the literature, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, Gradient Boosting (GB), and Neural Network (NN), are utilized to develop ML models. These models are then trained and tested on the prepared dataset to evaluate their performance in detecting ASD. The performance of the ML models is assessed using various metrics, such as Accuracy, Recall, Precision, F1-score, AUC, Train time, and Test time. These metrics provide insights into the models' overall accuracy, sensitivity, specificity, and the trade-off between true positive and false positive rates. The results of the study highlight the effectiveness of utilizing traditional feature selection approaches based on affected features. Specifically, the GB model outperforms the other models with an accuracy of 87%, Recall of 87%, Precision of 86%, F1-score of 86%, AUC of 95%, Train time of 21.890, and Test time of 0.173. Additionally, a benchmarking analysis against five other studies reveals that the proposed methodology achieves a perfect score across three key areas. By considering affected features through traditional feature selection approaches, the developed ML models demonstrate improved performance and have the potential to enhance ASD screening and diagnosis processes.

A Machine Learning Method for Prediction of Yogurt Quality and Consumers Preferencesusing Sensory Attributes and Image Processing Techniques

Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share and reduce any gap in food safety standards. In this paper, we develop a machine learning method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images using image processing texture and color feature extraction techniques. We compare three unsupervised ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique (Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the supervised ML feature selection technique over the traditional feature selection techniques.

Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection

We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. This is in contrast to the typical setting of quantum algorithms for optimization where a classical objective function is provided as a given Quadratic Unconstrained Binary Optimization problem and mapped to a sum of Pauli operators. Furthermore, we provide theoretical justification for our method based on convergence guarantees of quantum imaginary time evolution.To investigate the performance of our algorithm and its potential advantages, we tackle a challenging real-world optimization problem: feature selection. This refers to the problem of selecting a subset of relevant features to use for constructing a predictive model such as fraud detection. Optimal feature selection---when formulated in terms of a generic loss function---offers little structure on which to build classical heuristics, thus resulting primarily in ‘greedy methods’. This leaves room for (near-term) quantum algorithms to be competitive to classical state-of-the-art approaches. We apply our quantum-optimization-based feature selection algorithm, termed VarQFS, to build a predictive model for a credit risk data set with 20 and 59 input features (qubits) and train the model using quantum hardware and tensor-network-based numerical simulations, respectively. We show that the quantum method produces competitive and in certain aspects even better performance compared to traditional feature selection techniques used in today's industry.

IS-DT: A New Feature Selection Method for Determining the Important Features in Programmatic Buying

Traditional data-driven feature selection techniques for extracting important attributes are often based on the assumption of maximizing the overall classification accuracy. However, the selected attributes are not always meaningful for practical problems. So, we need additional confirmation from the experts in the domain knowledge to determine whether these extracted features are meaningful knowledge. Moreover, due to advances in mobile devices and wireless environments, programmatic buying (PB) has become one of the critical consumer behaviors in e-commerce. However, it is extremely difficult for PB service providers to build customers’ loyalty, since PB customers require a high level of service quality and can quickly shift the purchases from one website to another. Previous studies developed various dimensions/models to measure the service quality of PB; nevertheless, they did not identify the key factors for increasing customers’ loyalty and satisfaction. Consequently, this study used an importance–satisfaction (IS) model as domain knowledge and proposed a new IS-DT feature selection method. This new IS-DT method combined the IS model and the decision tree (DT) algorithm to extract useful service quality factors for enhancing customer satisfaction and loyalty in PB. An actual case was also provided to illustrate the effectiveness of our proposed method. The results showed that for increasing customer satisfaction, the highest impact factors included “problem solving”, “punctuality”, “valence”, and “ease of use”; for building customer loyalty, the most important factors were “expertise”, “problem solving”, “information”, “single column”, “voice guidance”, “QR code”, “situation”, “tangibles”, “assurance”, “entertainment”, and “safety”. Our IS-DT method can effectively determine important service quality factors in programmatic buying.

MRNA Biomarkers for Invasive Breast Cancer based on a Deep Feature Selection Approach

Early detection of breast cancer and effective identification of its correct stage remain major challenges for healthcare professionals. Testing the tumour for Oestrogen Receptor and Progesterone Receptor is a standard part of the initial evaluation of breast cancer diagnosis and treatment planning. Several expression profiling studies have illustrated that the expression of these hormone receptors is linked with diverse genetic variations, which means that several mutated genes can a affect the development and progression of breast cancer and contribute to its heterogeneity. Unfortunately, due to the high dimensionality and low sample size nature of microarray data, traditional statistical feature selection techniques fail to identify genes that could act as risk factors for breast cancer. Inspired by this, we developed a deep learning-based feature extraction module with a weight interpretation method to select a subset of robust biomarkers across three different mRNA expression data sets from The Cancer Genome Atlas program (TCGA). For a discovered feature (a gene) to be accepted for further investigation, it must have been independently selected by the weight interpretation method from each of the deep feature extraction modules (each having been trained on a different data set). The small panel of discovered biomarkers was then subsequently evaluated using a range of classifiers to ascertain their predictive ability with respect to the above hormone receptor status. We observed strong evidence that the upregulation in the expression levels of highly positively weighted genes within the deep feature selection modules and the down regulation in the expression levels of the highly negatively weighted genes both indicated the strong likelihood of a patient experiencing ER+/PR+ invasive breast cancer. In addition, we discovered a number of potentially novel biomarkers worthy of further consideration.

A novel feature selection technique for enhancing performance of unbalanced text classification problem

Since the last few decades, Text Classification (TC) is being witnessed as an important research direction due to the availability of a huge amount of digital text documents on the web. It would be tedious to manually organize and label them by human experts. Again digging a large number of highly sparse terms and skewed categories present in the documents put a lot of challenges in the correct labeling of the unlabeled documents. Hence feature selection is an essential aspect in text classification, which aims to select more concise and relevant features for further mining of the documents. Additionally, if the text in the document set is associated with multiple categories and the distribution of classes in the dataset is unbalanced, it imposes more challenges on the suitable selection of features for text classification. In this paper, a Modified Chi-Square (ModCHI) based feature selection technique is proposed for enhancing the performance of classification of multi-labeled text documents with unbalanced class distributions. It is an improved version of the Chi-square (Chi) method, which emphasizes selecting maximum features from the classes with a large number of training and testing documents. Unlike Chi, in which the top features are selected with top Chi value, in this proposed technique a score is calculated by considering the total number of relevant documents corresponding to each class with respect to the total number of documents in the original dataset. According to the score the features related to the highly relevant classes as well as high Chi-square value are selected for further processing. The proposed technique is verified with four different classifiers such as Linear SVM (LSVM), Decision tree (DT), Multilevel KNN (MLKNN), Random Forest (RF) over Reuters benchmark multi-labeled, multi-class, unbalanced dataset. The effectiveness of the model is also tested by comparing it with four other traditional feature selection techniques such as term frequency-inverse document frequency (TF-IDF), Chi-square, and Mutual Information (MI). From the experimental outcomes, it is clearly inferred that LSVM with ModCHI produces the highest precision value of 0.94, recall value of 0.80, f-measure of 0.86 and the least hamming loss value of 0.003 with a feature size 1000. The proposed feature selection technique with LSVM produces an improvement of 3.33%, 2.19%, 16.25% in the average precision value, 3.03%, 33.33%, 21.42% in the average recall value, 4%, 34.48%, 14.70% in average F-measure value and 14%, 37.68%, 31.74% in average hamming loss value compared to TF-IDF, Chi and MI techniques respectively. These findings clearly interpret the better performance of the proposed feature selection technique compared to TF_IDF, Chi and MI techniques on the unbalanced Reuters Dataset.

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does not consider the relatedness. During feature selection, all features are misread as outputs. Accordingly, a less optimal solution is achieved. In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO). First, decomposition of features into subsets based on relatedness in variables is performed. Random differential grouping is performed using a fitness value of two variables. Now, every subset is regarded as a population in GWO techniques. The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research. Once the features are optimized, we classify using advanced kNN process for accurate data classification. The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm. The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.

Feature selection for imbalanced data with deep sparse autoencoders ensemble

AbstractClass imbalance is a common issue in many domain applications of learning algorithms. Oftentimes, in the same domains it is much more relevant to correctly classify and profile minority class observations. This need can be addressed by feature selection (FS), that offers several further advantages, such as decreasing computational costs, aiding inference and interpretability. However, traditional FS techniques may become suboptimal in the presence of strongly imbalanced data. To achieve FS advantages in this setting, we propose a filtering FS algorithm ranking feature importance on the basis of the reconstruction error of a deep sparse autoencoders ensemble (DSAEE). We use each DSAE trained only on majority class to reconstruct both classes. From the analysis of the aggregated reconstruction error, we determine the features where the minority class presents a different distribution of values w.r.t. the overrepresented one, thus identifying the most relevant features to discriminate between the two. We empirically demonstrate the efficacy of our algorithm in several experiments, both simulated and on high‐dimensional datasets of varying sample size, showcasing its capability to select relevant and generalizable features to profile and classify minority class, outperforming other benchmark FS methods. We also briefly present a real application in radiogenomics, where the methodology was applied successfully.

Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

A Real Estate Valuation Model Using Boosted Feature Selection

To estimate real estate values, a complex valuation model based on artificial neural network (ANN) has been established as a successful means in modern machine learning research, specifically when high-dimensional data are available. Unfortunately, the real estate data in many locations, such as Thailand, are quite limited in terms of features. Hence, it becomes mandatory to reduce the complexity using feature selection techniques. These techniques aim to improve performance by identifying significant factors and help decrease the computational overload and model construction. However, due to the lack of explicability and interpretability in ANNs, the analysis of input factors cannot be explained directly by model composition. In this research, we apply a combination of a boosting strategy and input sensitivity analysis in an improved Garson's algorithm to perform feature selection that can adjust its selection criteria through each iteration on an ANN model. This proposed technique is then compared with other traditional feature selection techniques using synthetic data and real-world house valuation data. The results show that our model can maintain the sensitivity coefficient for every informative feature. The technique of this study provides a set of features that influences the house price and implies the character of each specific area. It also contributes to statistical improvement in most valuation cases, with a lower error than that obtained without a selected feature dataset, and it placed in the top 24% of the public leaderboard in the Zillow Prize competition.

Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records.

A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for preventative measures to alleviate said strain. Electronic health records provide opportunity for big data analysis to address such applications. Such data however, provides a challenging problem space for traditional statistics and machine learning due to high dimensionality and sparse data elements. This article proposes a novel machine learning methodology: entropy regularization with ensemble deep neural networks (ECNN), which simultaneously provides high predictive performance of hospitalization of patients with dementia whilst enabling an interpretable heuristic analysis of the model architecture, able to identify individual features of importance within a large feature domain space. Experimental results on health records containing 54,647 features were able to identify 10 event indicators within a patient timeline: a collection of diagnostic events, medication prescriptions and procedural events, the highest ranked being essential hypertension. The resulting subset was still able to provide a highly competitive hospitalization prediction (Accuracy: 0.759) as compared to the full feature domain (Accuracy: 0.755) or traditional feature selection techniques (Accuracy: 0.737), a significant reduction in feature size. The discovery and heuristic evidence of correlation provide evidence for further clinical study of said medical events as potential novel indicators. There also remains great potential for adaption of ECNN within other medical big data domains as a data mining tool for novel risk factor identification.

Feature grouping and selection: A graph-based approach

Most current feature selection techniques are focused on the incremental inclusion or exclusion of single individual features with respect to the candidate feature subset(s). The use of such approaches, where only the individual inclusion/exclusion of features is considered, means that information such as the collaborative contribution or correlation between features may be lost. The result is that the final selected feature subset may contain high levels of inter-feature redundancy, assuming that the key information embedded in the original feature set can still be retained. To address this problem, a general framework based on graph processing and three-way mutual information metrics is proposed in this paper that works by clustering similar features into groups, from which representative features are then drawn. Two different feature selection techniques based on this framework are presented: one by straightforward selection of representative features from the resulting feature groups and the other via a music-inspired metaheuristic search. Comparative experimental evaluation against traditional feature selection techniques over a diverse range of 20 benchmark datasets demonstrates the efficacy of the proposed approach. With these implementations, significant performance gains can be made in terms of classification accuracy in general and dimensionality reduction in particular while retaining feature semantics and considerably lessening the redundancy in the returned feature subsets.

Online streaming feature selection with incremental feature grouping

AbstractToday, the dimensionality of data is increasing in a massive way. Thus, traditional feature selection techniques are not directly applicable. Consequently, recent research has led to the development of a more efficient approach to the selection of features from a feature stream, known as streaming feature selection. Another active research area, related to feature selection, is feature grouping. Feature grouping selects relevant features by evaluating the hidden information of selected features. However, although feature grouping is a promising technique, it is not directly applicable to feature streams. In this paper, we propose a novel and efficient algorithm that uses online feature grouping, embedded within a new incremental technique, to select features from a feature stream. This technique groups similar features together; it assigns new incoming features to an existing group or creates a new group. To the best of our knowledge, this is the first approach that proposes the use of incremental feature grouping to perform feature selection from features. We have implemented this algorithm and evaluated it, using benchmark datasets, against state‐of‐the‐art streaming feature selection algorithms that use feature grouping or incremental selection techniques. The results show superior performance by the proposed technique through combining the online selection and grouping, in terms of prediction accuracy and running time.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Data Preprocessing Technologies > Classification Technologies > Machine Learning

Detecting biomarkers from microarray data using distributed correlation based gene selection

Over the past few decades, DNA microarray technology has emerged as a prevailing process for early identification of cancer subtypes. Several feature selection (FS) techniques have been widely applied for identifying cancer from microarray gene data but only very few studies have been conducted on distributing the feature selection process for detecting cancer subtypes. Not all the gene expressions are needed in prediction, this research article objective is to select discriminative biomarkers by using distributed FS method which helps in accurately diagnosis of cancer subtype. Traditional feature selection techniques have several drawbacks like unrelated features that could perform well in terms of classification accuracy with a suitable subset of genes will be left out of the selection. To overcome the issue, in this paper a new filter-based method for gene selection is introduced which can select the highly relevant genes for distinguishing tissues from the gene expression dataset. In addition, it is used to compute the relation between gene-gene and gene-class and simultaneously identify subset of essential genes. Our method is tested on Diffuse Large B cell Lymphoma (DLBCL) dataset by using well-known classification techniques such as support vector machine, naïve Bayes, k-nearest neighbor, and decision tree. Results on biological DLBCL dataset demonstrate that the proposed method provides promising tools for the prediction of cancer type, with the prediction accuracy of 97.62%, precision of 94.23%, sensitivity of 94.12%, F-measure of 90.12%, and ROC value of 99.75%. The experimental results reveal the fact that the proposed method is significantly improved classification accuracy and execution time, compared to existing standard algorithms when applied to the non-partitioned dataset. Furthermore, the extracted genes are biologically sound and agree with the outcome of relevant biomedical studies.

A novel hybrid wrapper–filter approach based on genetic algorithm, particle swarm optimization for feature subset selection

The classification is one of the main technique of machine learning science. In many problems, the data sets have a high dimensionality that the existence of all features is not important to the purpose of the problem, and this will decrease the accuracy and performance of the algorithm. In this situation, the feature selection will play a significant role, and by eliminating unrelated features, the efficiency of the algorithm will be increased. A hybrid filter-wrapper method is proposed in the present study for feature subset selection established with integration of evolutionary based genetic algorithms (GA) and particle swarm optimization (PSO). The presented method mainly aims to reduce the complication of calculation and the search time expended to achieve an optimum solution to the high dimensional datasets feature selection problem. The proposed method, named smart HGP-FS, utilizes artificial neural network (ANN) in the fitness function. The filter and wrapper methods are integrated in order to take the benefit of filter technique acceleration and the wrapper technique vigor for selection of dataset efficacious characteristics. Some dataset characteristics are eliminated through the filter phase, which in turn reduces complex computations and search time in the wrapper phase. Comparisons have been made for the effectiveness of the proposed hybrid algorithm with the usability of three hybrid filter-wrapper methods, two pure wrapper algorithms, two pure filter procedures, and two traditional wrapper feature selection techniques. The findings obtained over real-world datasets show the efficiency of the presented algorithm. The outcomes of algorithm examination on five datasets reveal that the developed method is able to obtain a more accurate classification and to remove unsuitable and unessential characteristics more effectively relative to the other approaches.