Bug Report Classification by Selecting Relevant Features Using Chi Square, Information Gain and Latent Semantic Analysis

Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction. In machine learning, feature selection plays a vital role in improving model performance, reducing overfitting, enhancing interpretability, and making the learning process more efficient. It helps extract meaningful patterns from drug and protein data while eliminating irrelevant or redundant information, resulting in more effective machine-learning models. On the other hand, classification is of great importance as it enables pattern recognition, decision-making, predictive modeling, anomaly detection, data exploration, and automation. It empowers machines to make accurate predictions and facilitates efficient decision-making in DPI prediction. For this research work, protein data was sourced from the KEGG database, while drug data was obtained from the DrugBank data machine-learning base. To address the issue of imbalanced Drug Protein Pairs (DPP), different balancing techniques like Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive SMOTE were employed. Given the large number of features associated with drugs and proteins, feature selection becomes necessary. Various feature selection methods were evaluated: Correlation, Information Gain (IG), Chi-Square (CS), and Relief. Multiple classification methods, including Support Vector Machines (SVM), Random Forest (RF), Adaboost, and Logistic Regression (LR), were used to predict DPI. Finally, this research identifies the best balancing, feature selection, and classification methods for accurate DPI prediction. This comprehensive approach aims to overcome the limitations of existing methods and provide more reliable and efficient predictions in drug-protein interaction studies.

High-dimensional datasets can lead to overfitting and computationally expensive model building on machine learning. This study uses a dimensionality reduction technique, namely feature selection techniques, to overcome these problems. Five feature selection methods were used, i.e., Chi-Square (CS), Information Gain (IG), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Least Absolute Shrinkage and Selection Operator (LASSO), and three classifier methods viz. Naïve Bayes, Extreme Gradient Boosting (XGB), and RF Classifier. The dataset used is the Heart Attack Analysis & Prediction Dataset. In this study, three scenarios of the best feature selection were carried out, namely: 1. selection of the best feature using a specific feature selection, 2. the intersection of selection of the best feature from the same category, 3. the intersection of selection of the best feature from the five proposed feature selection methods. The performance model is measured using accuracy, precision, recall, f1-score, AUC, and training time. This study reveals that feature selection is very effective in improving the performance of prediction models. Based on the experiment results, the best feature selection is CS and IG in the Filter Category with the XGB model. The best feature selected improved the performance of accuracy, precision, recall, f1-score, and AUC, i.e., 1.7%, 1%, 2.3%, 1.6%, and 0.2%, respectively. Meanwhile, training time requirements decreased by 23.5%. Feature selection with specific techniques performs better than feature selection by selecting the best features from the same category feature selection technique or various other feature selection methods.

Background: Cholangiocarcinoma is a molecularly heterogenous cancer arising from the biliary epithelium. Individual omic techniques have identified clinically relevant, targetable subtypes but most tumors have no targetable alterations based on exome or transcriptomic evaluation. Integrated multiomics approaches incorporating transcriptomic, proteomic, and phosphoproteomic characterization provide deeper understanding and identify non-mutated, activated pathways that can be targeted. To optimize the potential clinical utility of these approaches, minimal classifier features must be defined. Methods: Approximately 60,000 RNA, protein, and phosphoprotein features were extracted from 35 cholangiocarcinomas treated at the primary US study site. 206 patients treated and profiled at an institution in China were also included. Using the Multiomics Factor Analysis clustering method, patients were sorted into 3 distinct molecular profiles. Important molecular features were selected using competing feature selection (FS) methods: Boruta, RreliefF, variable selection using random forests (RF), recursive factor elimination (RFE), median decrease accuracy in RF, and Concrete Autoencoders. Machine learning classification prediction models including Extreme Gradient Boosting, gradient-boosted trees, multinomial logit, and support vector machine (SVM) were tested on each FS. Each combination of FS and predictive model was tested with 10 replicates of 10-fold cross-validation of a randomly-selected balanced test set and an oversampled, balanced training subset. Accuracy, F1 score, AUC, precision, and recall were assessed for each FS and predictive model combination. A custom function of physical assay cost and classification accuracy was optimized to find the best assay. KEGG pathway overrepresentation analysis was used to analyze feature subsets. Results: A consistent set of 50 important RNA and protein features was identified, including previously reported prognostic factors, like SpryD4 protein expression, as well as previously unreported pathway surrogates, such as Slc2A2 or Pipox protein. Pathway overrepresentation analysis found insulin signaling and resistance and amino acid biosynthesis/metabolism pathways were overrepresented in the discriminative feature set. The gradient-boosted trees algorithm combined with the RFE FS method had the best performance, with a multiclass macro average (±sd) cross-validation accuracy 89% (±6%), F1 score 89% (±8%), AUC 0.98 (±0.02), precision 90% (±10%), recall 89% (±6%). Conclusions: Feature selection methods and predictive machine learning models work synchronously to develop accurate molecular subtype assays based on just 50 protein and RNA levels. This strategy has the potential to make molecular subtyping for cholangiocarcinoma and other molecularly complex cancers fast and widely available. Citation Format: Ellen Larson, Erik Jessen, Dong-Gi Mun, Jennifer Tomlinson, Amro Abdelrahman, Danielle Carlson, Hojjat Salehinejad, Rory Smoot. Feature selection and machine learning strategies optimize an affordable molecular assay for cholangiocarcinoma subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1107.

Software defect prediction models are used for predicting high risk software components. Feature selection has significant impact on the prediction performance of the software defect prediction models since redundant and unimportant features make the prediction model more difficult to learn. Ensemble feature selection has recently emerged as a new methodology for enhancing feature selection performance. This paper proposes a new multi-criteria-decision-making (MCDM) based ensemble feature selection (EFS) method. This new method is termed as MCDM-EFS. The proposed method, MCDM-EFS, first generates the decision matrix signifying the feature’s importance score with respect to various existing feature selection methods. Next, the decision matrix is used as the input to well-known MCDM method TOPSIS for assigning a final rank to each feature. The proposed approach is validated by an experimental study for predicting software defects using two classifiers K-nearest neighbor (KNN) and naïve bayes (NB) over five open-source datasets. The predictive performance of the proposed approach is compared with existing feature selection algorithms. Two evaluation metrics – nMCC and G-measure are used to compare predictive performance. The experimental results show that the MCDM-EFS significantly improves the predictive performance of software defect prediction models against other feature selection methods in terms of nMCC as well as G-measure.

Stroke Treatment Prediction Using Features Selection Methods and Machine Learning Classifiers

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

Adolescents in higher education are more prone to Avoidant Personality Disorder (AVPD), which strongly affects academic achievement. The goal of this study was to create models for accurate prediction of the likelihood of Avoidant Personality Disorder among students in higher education. Information Gain, Gain Ratio, and Wrapper Approach are used as feature selection methods combined with data resampling techniques and machine learning, including Multi-Layer Perceptron Neural Network, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine. The findings revealed that the Wrapper approach gave higher accuracy than Information Gain and Gain Ratio approach. Further, using the Gain Ratio approach gives the model a slightly higher efficiency than the Information Gain. Furthermore, when comparing feature selection and data resampling, it was found that the model using feature selection had more higher model efficiency than data resampling alone. Additionally, combining the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbor (SMOTE-ENN) considerably increased the model’s effectiveness. Finally, the model’s efficiency was at its maximum, with an accuracy of 95.52%, when the Wrapper approach was used in conjunction with the Synthetic Minority Over-sampling Technique, the Edited Nearest Neighbor algorithm, and the Multi-Layer Perceptron Neural Network.

Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms.

Parkinson’s disease is a neurodegenerative disease that is associated with genetic and environmental factors. However, the genes causing this degeneration have not been determined, and no reported cure exists for this disease. Recently, studies have been conducted to classify diseases with RNA-seq data using machine learning, and accurate diagnosis of diseases using machine learning is becoming an important task. In this study, we focus on how various feature selection methods can improve the performance of machine learning for accurate diagnosis of Parkinson’s disease. In addition, we analyzed the performance metrics and computational costs of running the model with and without various feature selection methods. Experiments were conducted using RNA sequencing—a technique that analyzes the transcription profiling of organisms using next-generation sequencing. Genetic algorithms (GA), information gain (IG), and wolf search algorithm (WSA) were employed as feature selection methods. Machine learning algorithms—extreme gradient boosting (XGBoost), deep neural network (DNN), support vector machine (SVM), and decision tree (DT)—were used as classifiers. Further, the model was evaluated using performance indicators, such as accuracy, precision, recall, F1 score, and receiver operating characteristic (ROC) curve. For XGBoost and DNN, feature selection methods based on GA, IG, and WSA improved the performance of machine learning by 10.00% and 38.18%, respectively. For SVM and DT, performance was improved by 0.91% and 7.27%, respectively, with feature selection methods based on IG and WSA. The results demonstrate that various feature selection methods improve the performance of machine learning when classifying Parkinson’s disease using RNA-seq data.

Researches on sentiment analysis are growing to a great extent and attracting wide ranges of attention from academics and industries as well. Feature generation and selection are consequent for text mining as the high dimensional feature set can affect the performance of sentiment analysis. This paper exhibits the efficacy of the proposed combined feature selection technique on machine learning classification algorithms over their individual usefulness. Initially, we transform the review datasets into the feature vector of unigram features along with bi-tagged features based on POS pattern. Next, information gain (IG), Chi squared (χ2) and minimum redundancy maximum relevancy (mRMR) feature selection methods are applied to obtain an optimal feature subset for further functionality. These features are then given input to multiple machine learning classifiers, namely, support vector machine (SVM), multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB) and logistic regression (LR) on multi domain product review datasets. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the feature selection method mRMR with SVM achieved a better accuracy of 91.39, which is encouraging and comparable to the related research.

We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods. This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results. For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set. Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods. • Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions. • Radiomics model based on multiparametric MRI has better performance than single-sequence models. • The machine learning methods RFE with SVM perform best in the current cohort.

Feature selection plays a critical role in improving the efficiency, accuracy, and interpretability of machine learning models, particularly when dealing with high-dimensional datasets. Among various approaches, wrapper-based feature selection methods are known for their ability to capture feature interactions by directly optimizing model performance. This study presents a comprehensive comparative analysis of six wrapper feature selection techniques—Recursive Feature Elimination (RFE), Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE)—in conjunction with five widely used classification algorithms: Decision Tree, K-Nearest Neighbour, Random Forest, Logistic Regression, and Support Vector Machine. Experiments are conducted on an educational dataset comprising 395 student records with 30 attributes obtained from the UCI repository, using different feature subset sizes (all features, top 20, top 15, and top 10). Model performance is evaluated using accuracy, precision, recall, F1-score, and AUC. The results demonstrate that wrapper methods significantly enhance classification performance while reducing dimensionality, with GA and RFE consistently emerging as the most effective techniques across multiple classifiers. DE also shows strong performance, particularly with Logistic Regression and Random Forest, whereas PSO generally underperforms in terms of AUC. Furthermore, reducing the feature set does not adversely affect predictive accuracy and, in several cases, leads to improved generalization. The findings confirm the effectiveness of wrapper methods for educational data mining and provide practical insights for selecting optimal feature–classifier combinations.

HighlightsE-nose and computer vision combined with data fusion strategies were applied to trace tea origins.Pearson correlation analysis, IG, and F-scores were applied to modify the fusion strategies.The classification performances of different fusion strategies were compared.The strategies of IG_SVM_FL and IG_SVM_DS achieved the best results.Abstract. The traceability of tea origins is of great significance. In this study, an electronic nose (E-nose) and computer vision system (CVS) were jointly applied to acquire aroma and image signals of tea samples, aiming at identifying Longjing teas from different geographic origins including Jinyun (120° 7' E, 28° 65' N), Xihu (120° 13' E, 30° 27' N), Xinchang (120° 9' E, 29° 50' N), and Qian Daohu (119° 3' E, 29° 60' N). Data fusion was used to integrate the E-nose and CVS signals for comprehensively characterizing the tea samples. Four traditional fusion strategies including k-nearest neighbors (KNN) and support vector machine (SVM) based feature-level fusion (KNN_FL and SVM_FL) and Dempster-Shafer (D-S) evidence theory based decision-level strategies (KNN_DS and SVM_DS) were applied for classification modeling. Pearson analysis, information gain (IG), and F-scores were employed to modify the traditional fusion strategies to reduce inconsistent and redundant information in the fusion process. The results indicated that the original fusion strategies had no superiority over independent E-nose and CVS decision-making. With the feature selection methods, the modified fusion strategies generally exhibited better performance than the independent decision-making and original fusion strategies. Moreover, the IG-based fusion strategies, encompassing IG_SVM_FL and IG_SVM_DS, achieved the highest classification accuracy of 100%. Keywords: Computer vision, Electronic nose, Feature selection, Fusion strategies, Tea origins.

Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposed methods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen product review dataset. Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a composite features vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI), and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking to the features depending on their score; thus a prominent feature vector (CompIG, CompGI, and CompCHI) can be generated easily for classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy.

Chronic kidney disease (CKD) involves numerous variables, but only a few significantly impact the classification task. The statistically equivalent signature (SES) method, inspired by constraint-based learning of Bayesian networks, is employed to identify essential features in CKD. Unlike conventional feature selection methods, which typically focus on a single set of features with the highest predictive potential, the SES method can identify multiple predictive feature subsets with similar performance. However, most feature selection (FS) classifiers perform suboptimally with strongly correlated data. The FS approach faces challenges in identifying crucial features and selecting the most effective classifier, particularly in high-dimensional data. This study proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) in conjunction with the SES method for feature selection in CKD identification. Following this, an ensemble deep-learning model combining long short-term memory (LSTM) and gated recurrent unit (GRU) networks is proposed for CKD classification. The features selected by the hybrid feature selection method are fed into the ensemble deep-learning model. The model’s performance is evaluated using accuracy, precision, recall, and F1 score metrics. The experimental results are compared with individual classifiers, including decision tree (DT), Random Forest (RF), logistic regression (LR), and support vector machine (SVM). The findings indicate a 2% improvement in classification accuracy when using the proposed hybrid feature selection method combined with the LSTM and GRU ensemble deep-learning model. Further analysis reveals that certain features, such as HEMO, POT, bacteria, and coronary artery disease, contribute minimally to the classification task. Future research could explore additional feature selection methods, including dynamic feature selection that adapts to evolving datasets and incorporates clinical knowledge to enhance CKD classification accuracy further.