Wrapper Method Research Articles

Prognosis of Cervical Cancer Disease by Applying Machine Learning Techniques

Cervical cancer is one of the deadliest diseases in women worldwide. It is caused by long-term infection of the skin cells and mucosal cells of the genital area of women. The most disturbing thing about this cancer is the fact that it does not show any symptoms when it occurs. In the diagnosis and prognosis of cervical cancer disease, machine learning has the potential to help detect it at an early stage. In this paper, we analyzed different supervised machine learning techniques to detect cervical cancer at an early stage. To train the machine learning model, a cervical cancer dataset from the UCI repository was used. The different methods were evaluated using this dataset of 858 cervical cancer patients with 36 risk factors and one outcome variable. Six classification algorithms were applied in this study, including an artificial neural network, a Bayesian network, an SVM, a random tree, a logistic tree, and an XG-boost tree. All models were trained with and without a feature selection algorithm to compare the performance and accuracy of the classifiers. Three feature selection algorithms were used, namely (i) relief rank, (ii) wrapper method and (iii) LASSO regression. The maximum accuracy of 94.94% was recorded using XG Boost with complete features. It is also observed that for this dataset, in some cases, the feature selection algorithm performs better. Machine learning has been shown to have advantages over traditional statistical models when it comes to dealing with the complexity of large-scale data and uncovering prognostic features. It offers much potential for clinical use and for improving the treatment of cervical cancer. However, the limitations of prediction studies and models, such as simplified, incomplete information, overfitting, and lack of interpretability, suggest that further efforts are needed to improve the accuracy, reliability, and practicality of clinical outcome prediction.

Correlation-based feature selection using bio-inspired algorithms and optimized KELM classifier for glaucoma diagnosis

Reduced computational time and cost, reduced skilled professional resources, and diagnostic accuracy have made medical diagnosis using computer aided systems (CAD) increasingly popular and can be comfortably employed in the diagnosis of many acute and chronic diseases in ophthalmology, cardiology, cancer detection, etc. There seems to be a growing necessity for the computational algorithms to be robust enough, to identify the abnormality in each of the cases, aiding in early diagnosis. In this paper, a glaucoma diagnostic approach based on the wrapper method employing bio-inspired algorithms, and a Kernel-Extreme Learning Machine (KELM) classifier is proposed. The bio-inspired algorithms are deployed to select feature sub-sets, generating three feature sub-sets from the pre-processed fundus images by adopting a correlation-based feature selection (CFS) approach. The selected features are utilized to train the salp-swarm optimization based KELM, which finds the optimal parameters of the KELM classifier network. The proposed methodology is evaluated on the public and private retinal fundus datasets containing 7280 images. The experimental outcome revealed that the system is able to attain a maximum overall accuracy of 99.61% with 99.89% sensitivity and 100% specificity. A 5-fold cross validation showed 98.78% accuracy ensuring a bias-free classification. Further, by experimenting on degraded images (Gaussian, salt-pepper noise images) of the original dataset, the model achieved extreme robustness with 99.3% accuracy. The proposed method is compared with other similar methods, which showed the efficiency of our method. The framework proposed can aid in making clinical decisions for various pathologies like lung infection, diabetic retinopathy, etc.

A Sound-Based Fault Diagnosis Method for Railway Point Machines Based on Two-Stage Feature Selection Strategy and Ensemble Classifier

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification

Biological brain signals may be used to identify emotions in a variety of ways, with accuracy depended on the methods used for signal processing, feature extraction, feature selection, and classification. The major goal of the current work was to use an adaptive channel selection and classification strategy to improve the effectiveness of emotion detection utilizing brain signals. Using different features picked by feature fusion approaches, the accuracy of existing classification models' emotion detection is assessed. Statistical modeling is used to determine time-domain and frequency-domain properties. Multiclass classification accuracy is examined using Neural Networks (NNs), Lasso regression, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). After performing hyperparameter tuning, a remarkable increase in accuracy is achieved using Lasso regression, while RF performed well for all the feature sets. 78.02% and 76.77% accuracy were achieved for a small and noisy 24 feature dataset by Lasso regression and RF respectively whereas 76.54% accuracy is achieved by Lasso regression with the backward elimination wrapper method.

Methods of configuration test and deformation analysis for large airship

In recent years, high-altitude aerostats have been increasingly developed in the direction of multi-functionality and large size. Due to the large size and the high flexibility, new challenges for large aerostats have appeared in the configuration test and the deformation analysis. The methods of the configuration test and the deformation analysis for large airship have been researched and discussed. A tested method of the configuration, named internal scanning, is established to quickly obtain the spatial information of all surfaces for the large airship by the three-dimensional (3D) laser scanning technology. By using the surface wrap method, the configuration parameters of the large airship are calculated. According to the test data of the configuration, the structural dimensions such as the distances between the characteristic sections are measured. The method of the deformation analysis for the airship contains the algorithm of non-uniform rational B-splines (NURBS) and the finite element (FE) method. The algorithm of NURBS is used to obtain the reconfiguration model of the large airship. The seams are considered and the seam areas are divided. The FE model of the middle part of the large airship is established. The distributions of the stress and the strain for the large airship are obtained by the FE method. The position of the larger deformation for the airship is found.

MABUSE: A margin optimization based feature subset selection algorithm using boosting principles

Feature subset selection is one of the most common procedures in machine learning tasks. In a broad sense, feature selection methods can be classified into three major groups, embedded, filter and wrapper methods. Although wrappers might attain superior classification performances, they suffer from scalability issues as they are more computationally expensive than the other methods. Filters are typically faster, and sometimes they are the only applicable methods when the datasets are large. In the field of classification, margin optimization has been proven to be an efficient approach for improving the generalization performance of many classification models. Although margins have been used as criteria for feature selection, in most cases, the most advanced methods are wrappers, which suffer from high computational costs and do not outperform the faster algorithms. In this paper, we propose MABUSE, which is a feature selection method that optimizes margins from a filter perspective. We consider a nearest-neighbor margin definition and, borrowing from the strategy of classifier ensemble construction using boosting, we develop a new method that uses a simple heuristic search. Extensive experimental validation demonstrates that our proposed approach outperforms the state-of-the-art algorithms in both classification and reduction, and has a computational cost that is similar to previous algorithms.

Accuracy Enhancement for Breast Cancer Detection Using Classification and Feature Selection

Chronic disease like kidney failure, heart disease, cancer etc. is the major cause of deaths now days worldwide. Especially for the females the most dangerous type of disease from which the women of every age group are suffering especially the middle age group women’s is the breast cancer. To detect this type of disease at an early stage is a challenging task. In order to predict the breast cancer at an early stage classification algorithm of high accuracy and less error rate are desirable. In this research work we have used 4 classification algorithms K-NN, J48, Logistic regression and Bayes Net for building the predictive model, also the wrapper method of feature selection is used to enhance the accuracy rate and reduce the error rate of the used classifiers. To carry out this research we have used Wisconsin Diagnostic Breast Cancer dataset which contains 569 instances along with 32 attributes and a class attribute which will predict the type of cancer i.e. Benign or Malignant.

A hybrid approach for cancer classification based on squirrel search

As thousands of gene components are present in microarray statistics, the ability to select a smaller subset of features is critical for phenotyping classification. Genes can be ranked according to their phenotypic effects using techniques that are frequently reused. Scenario microarray research typically only includes a few dozen samples while each microarray can include thousands of genes. When the number of features exceeds the number of illustrations, standard classifiers fail to perform well. When developing classifiers, it can be helpful to focus on features that are most relevant to distinguishing between the two categories. The features we have collected have some redundancy, so we are looking for ways to eliminate them. Recommendations are given for feature selection frameworks that have low severance and high importance. Filter and wrapper methods are used in this study, and a hybrid approach is proposed by combining the physiognomies of the two stated methods of gene selection simulation. For the most part, microarray data will be sorted using Squirrel Search (SS)-based optimization. We compared the filter, wrapper, and hybrid models’ gene collection performance in the same way. Study results from four gene expression datasets showed that class predictions were greatly improved. Among the three classification methods, improvements are reliable in experiments.

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.

Comparison and Analysis of Acoustic Features of Western and Chinese Classical Music Emotion Recognition Based on V-A Model

Music emotion recognition is increasingly becoming important in scientific research and practical applications. Due to the differences in musical characteristics between Western and Chinese classical music, it is necessary to investigate the distinctions in music emotional feature sets to improve the accuracy of cross-cultural emotion recognition models. Therefore, a comparative study on emotion recognition in Chinese and Western classical music was conducted. Using the V-A model as an emotional perception model, approximately 1000 pieces of Western and Chinese classical excerpts in total were selected, and approximately 20-dimension feature sets for different emotional dimensions of different datasets were finally extracted. We considered different kinds of algorithms at each step of the training process, from pre-processing to feature selection and regression model selection. The results reveal that the combination of MaxAbsScaler pre-processing and the wrapper method using the recursive feature elimination algorithm based on extremely randomized trees is the optimal algorithm. The harmonic change detection function is a culturally universal feature, whereas spectral flux is a culturally specific feature for Chinese classical music. It is also found that pitch features are more significant for Western classical music, whereas loudness and rhythm features are more significant for Chinese classical music.

A Hybrid Approach Variable Selection Algorithm Based on Mutual Information for Data-Driven Industrial Soft-Sensor Applications


 
 
 
 The development of virtual sensors predicting the desired output requires a careful selection of input variables for model construction. In an industrial environment, datasets contain many instrumentation system measures; however, these variables are often non-relevant or excessive information. This paper proposes a variable selection algorithm based on mutual information examination, redundancy analysis, and variable reduction for soft-sensor modeling. A relevance calculation is performed in the first stage to select important variables using the mutual information criterion. Then, the detection and exclusion of redundant variables are carried out, penalizing undesired variables. Finally, the most relevant variables subset is determined through a wrapper method using Mallowssans' Cp metric to assess the fitting prediction performance. The approach was successfully applied to estimate the ethanol concentration for a distillation column process using an adaptive network-based fuzzy inference system architecture as a non-linear dynamic regression model. A comparative study was performed considering the application of correlation analysis and the method proposed in this study. Simulation results show the effectiveness of the proposed approach in the variable selection providing a reduction in search of suitable models that achieve faster results for developing soft sensors oriented to industrial applications.
 
 
 

Forward feature selection for toxic speech classification using support vector machine and random forest

<span lang="EN-US">This study describes the methods for eliminating irrelevant features in speech data to enhance toxic speech classification accuracy and reduce the complexity of the learning process. Therefore, the wrapper method is introduced to estimate the forward selection technique based on support vector machine (SVM) and random forest (RF) classifier algorithms. Eight main speech features were then extracted with derivatives consisting of 9 statistical sub-features from 72 features in the extraction process. Furthermore, Python is used to implement the classifier algorithm of 2,000 toxic data collected through the world's largest video sharing media, known as YouTube. Conclusively, this experiment shows that after the feature selection process, the classification performance using SVM and RF algorithms increases to an excellent extent. We were able to select 10 speech features out of 72 original feature sets using the forward feature selection method, with 99.5% classification accuracy using RF and 99.2% using SVM.</span>

Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper

An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine marketing data and computer science methods is imperative to classify users’ needs. This work bridges the gap between computer and marketing science by introducing the current trends of AI models on marketing data. It examines consumers’ behaviour by using a decision-making model, which analyses the consumer’s choices and helps the decision-makers to understand their potential clients’ needs. This model is able to predict consumer behaviour both in the digital and physical shopping environments. It combines decision trees (DTs) and genetic algorithms (GAs) through one wrapping technique, known as the GA wrapper method. Consumer data from surveys are collected and categorised based on the research objectives. The GA wrapper was found to perform exceptionally well, reaching classification accuracies above 90%. With regard to the Gender, the Household Size, and Household Monthly Income classes, it manages to indicate the best subsets of specific genes that affect decision making. These classes were found to be associated with a specific set of variables, providing a clear roadmap for marketing decision-making.

Hybrid Optimization and Deep Learning for Detecting Fraud Transactions in the Bank

This paper, the efficient and effective fraud detection technique, termed SpiHWO-based Deep RNNtechnique is developed. At first, the data transformation is performed for transforming the data using Yeo-Johnson transformation. After that, the effective features are selected based on wrapper method where the best features are selected for further processing. Then, by using selected features, fraud detection is executed based on Deep RNN classifier, which is trained by developed SpiHWO technique. The proposed SpiHWO algorithm is newly developed by combining the SMO algorithm and HWO algorithm. Furthermore, the performance of developed method is computed using performance metrics, like sensitivity, specificity and also accuracy. The developed method achieved improved performance with respect to accuracy of 0.951, sensitivity of 0.985 and specificity of 0.792.

Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree

Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.

Prediction of Chronic Kidney Disease using Machine Learning

Chronic Kidney Disease is one of the most serious illnesses nowadays, and it is vital to have a good diagnosis as soon as possible. Machine learning has proven to be effective in medical therapy. The doctor can diagnose the ailment early with the use of machine learning classifier algorithms. This article has examined Chronic Kidney Disease prediction from this standpoint. The Chronic Kidney Disease dataset was obtained from the University of California at Irvine's repository. The artificial neural network, C5.0, Chi-square. After effectively filling out the incomplete data set, 4 machine learning algorithms (Random forest, Support vector machine, K-nearest neighbor and Decision tree) were used to establish models. The dataset was also subjected to the significant feature selection technique. The results were computed for each classifier using I full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, and (vi) synthetic minority over-sampling technique with full features. The results show that in synthetic minority over-sampling technique with full features, LSVM with penalty L2 has the maximum accuracy of 98.86 percent. Along with precision, recall, F-measure, and area, accuracy, precision, recall. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis..

Wrap & Plant Technology: An Innovative and Cost-Effective Method for Seed Yam Treatment for Nematode Control in Fields

ABSTRACT
 In West Africa, seed yam represents an important source of nematode inoculums in yam fields and a major cause of the disease perpetuation. To enhance nematode control on yam (Dioscorea spp.), a pilot participatory field evaluation was conducted in Benin on nematode control potential of abamectin-treated banana paper (treatment A) using seed wrap method. An additional seed yam wrapped with untreated paper (treatment B) is also considered. Both treatments A & B are then compared to unwrapped seed yam representing the farmers’ practice (FP). In each field, plots are arranged in a randomized complete block design with five replicates. The effects of seed yam treatment on nematode control in fields, gustatory qualities of yam tubers, and both social and profitability studies of the technology were assessed. In the present paper, only the study related to the profitability based on the Net Margin (NM) and the ratio Cost/Profit is presented. Results reveal advantages of treatments A and B over (FP). In fact, production statistics from treatments A and B are significantly higher than those from (FP). Based on the "willingness to pay" approach, the profitability study shows that treatment A has a Net Margin of approximately 153.7% that of (FP). Concerning the ratio Cost/Profit, 100 FCFA (West African CFA franc) spent in yam production using this new technology generates around 79.3 FCFA against 59.9 FCFA for (FP). Despite the success of this pilot study, input prices must be monitored before scaling up this innovation to the other agro-ecological regions of Benin.
 Key-words: Nematode control, Dioscorea spp., cost effectiveness, input prices, Wrap & plant technology, Benin

Lightweight Internet of Things Botnet Detection Using One-Class Classification.

Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation.

Selection of best feature reduction method for module-based software defect prediction

In the Software development life cycle (SDLC) the prediction of software defects is one of the crucial parts. Recent years have witnessed various studies for predicting software defects, and most of them involve machine learning techniques. Before predicting the defects some pre-processing steps are required, such as feature selection, outlier removal, and feature scaling as it helps in improving accuracy and reducing the execution time(ET). The present investigation focuses on feature selection which is a dimensionality reduction technique. Further, we have also proposed a framework on module-based software defect prediction using feature selection techniques. These techniques are divided into three categories i.e filter methods, wrapper methods, and hybrid methods that combine two wrapper methods i.e. Sequential Forward Selection and Sequential Backward Selection that we have developed. Finally, classification has been performed by KNN, Logistic Regression, Decision Tree, and SVM using the above-mentioned feature selection techniques on eight publicly available Promise datasets and compared them with the existing state-of-the-art(SOTA) methods. The result shows that the hybrid method performs better in terms of accuracy by 4.2%, 3.9%, 3.8% on datasets pc4, jm1, kc2 respectively when applied along with machine learning, as compared to filter and wrapper methods.

Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification.

A large number of features lead to very high-dimensional data. The feature selection method reduces the dimension of data, increases the performance of prediction, and reduces the computation time. Feature selection is the process of selecting the optimal set of input features from a given data set in order to reduce the noise in data and keep the relevant features. The optimal feature subset contains all useful and relevant features and excludes any irrelevant feature that allows machine learning models to understand better and differentiate efficiently the patterns in data sets. In this article, we propose a binary hybrid metaheuristic-based algorithm for selecting the optimal feature subset. Concretely, the brain storm optimization algorithm is hybridized by the firefly algorithm and adopted as a wrapper method for feature selection problems on classification data sets. The proposed algorithm is evaluated on 21 data sets and compared with 11 metaheuristic algorithms. In addition, the proposed method is adopted for the coronavirus disease data set. The obtained experimental results substantiate the robustness of the proposed hybrid algorithm. It efficiently reduces and selects the feature subset and at the same time results in higher classification accuracy than other methods in the literature.