Evaluating Feature Selection Techniques in Simulated Soccer Multi Agents System

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.

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.

We consider feature selection for text classification both theoretically and empirically. Our main result is an unsupervised feature selection strategy for which we give worst-case theoretical guarantees on the generalization power of the resultant classification function f with respect to the classification function f obtained when keeping all the features. To the best of our knowledge, this is the first feature selection method with such guarantees. In addition, the analysis leads to insights as to when and why this feature selection strategy will perform well in practice. We then use the TechTC-100, 20-Newsgroups, and Reuters-RCV2 data sets to evaluate empirically the performance of this and two simpler but related feature selection strategies against two commonly-used strategies. Our empirical evaluation shows that the strategy with provable performance guarantees performs well in comparison with other commonly-used feature selection strategies. In addition, it performs better on certain datasets under very aggressive feature selection.

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.

Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an intelligent system to facilitate the e-learning process in the educational environment. MAS is used to make interaction easily among agents, which supports the use of feature selection. The feature selection methods are used to select the important and relevant features from the database that could help machine learning algorithms produce high performance. This paper aims to propose an effective and suitable system for multiagent-based machine learning algorithms and feature selection methods to enhance the e-learning process in the educational environment which predicts pass or fail results. The univariate and Extra Trees feature selection methods are used to select the essential attributes from the database. Five machine learning algorithms named Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors algorithm (KNN) are applied to all features and selected features. The results showed that the learning algorithm that has been measured by the Extra Trees method has achieved the highest performance depending on the evaluation of cross-validation and testing.

Hyperspectral images (HSIs) generally contain a large amount of spectral bands (features), and the redundant information in them will cause the Hughes phenomenon in the classification process. And feature extraction and feature selection are the two main existing methods to effectively reduce the redundancy of spectral information in the field of HSIs classification. Compared with feature extraction methods, feature selection methods can preserve most of the features of the original HSIs data without losing their valuable details. However, most existing feature selection methods based on single scene (domain) perform poorly in some scenes (domains) with insufficient labeled samples. Therefore, how to adopt an efficient feature selection method to select the optimal feature subsets of source scene and target scene and use the sample information of source scene to assist in the classification of target scene so as to improve the classification accuracy of images in the target scene as much as possible is still very challenging. In order to solve the above problem, this paper proposes a new cross-scene algorithm: Improved Ant Colony Optimization Algorithm-Based Cross-Scene Feature Selection Algorithm (IMACO-CSFS). In order to obtain more accurate feature subsets of the two scenes, IMACO-CSFS proposes a priority sorting-based ant colony strategy to make the subsequent search process focus on the global optimal solution (optimal feature subset) found in the previous iteration. In addition, in order to further accelerate the convergence speed of the global optimal solution, an ant colony strategy based on elite ants is proposed in IMACO-CSFS to more efficiently obtain the optimal feature subsets of the two scenes for training the classifier. Furthermore, this paper simultaneously considers overall classification accuracies of the optimal feature subsets for both scenes and dynamically adjusts their scale to ensure the consistency of the selected features between the two scenes, attenuating the effect of spectral shift and achieving the higher image classification accuracy in the target scene. Experimental results on three cross-scene HSI data pairs demonstrate that IMACO-CSFS is superior in cross-scene feature selection and cross-scene HSIs classification.

Taking the Futian District as the research area, this study proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated. The results showed that feature selection can eliminate irrelevant features, increase the mean correlation between features slightly, and improve the classification accuracy and computational efficiency significantly. The recursive feature elimination-support vector machine (RFE-SVM) model obtained the best results, with an overall accuracy of 89.17% and a kappa coefficient of 0.8695, respectively. In addition, this study proved that the fusion of optical and SAR data can effectively improve mapping and reduce the confusion between different land covers. The novelty of this study is with the insight into the merits of multi-source data fusion and feature selection in the land cover mapping process over complex urban environments, and to evaluate the performance differences between different feature selection methods.

The trend of current brain computer interfaces (BCI) seek to establish bi-directional communication with the brain, for instance, recovering motor functions by externally controlling devices and directly stimulating the brain. This will greatly assist paralyzed individuals through bypassing the damaged brain region. The key process of this communication interface is to decode movements from neural signals and encode information into neural activity. The majority of decoding or pattern classification studies have focused on cortical areas for BCIs, but deep brain structures have also been involved in motor control. The subthalamic nucleus (STN) in the basal ganglia is involved in the preparation, execution and imagining of movements, and may be an alternative source for driving BCIs. This study therefore aimed to classify patterns of deep brain local field potentials (LFPs) related to execution of visually cued movements. LFPs were recorded bilaterally from the STN through deep brain stimulation electrodes implanted in patients with Parkinson's disease. The frequency dependent components of the LFPs were extracted using the wavelet packet transform. In each frequency component, signal features were extracted using an alternative approach called neural synchronization by analyzing Granger causality between the STN. Based on these extracted features, a new feature selection strategy, namely weighted sequential feature selection (WSFS) was developed to efficiently select the optimal feature subset. A support vector machine (SVM) classifier was implemented alongside this novel feature extraction and selection strategy, and evaluated using a cross-validation procedure. Using this optimised feature subset, average correct pattern classification accuracy of movement (left or right) reached 76.0±3.1%. The results obtained in this study are encouraging and suggest that the neural activity in the deep neural circuit (basal ganglia) can be used for controlling BCIs.

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.

Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240This article is categorized under: Technologies > Data Preprocessing

Last century, the challenge was to develop new technologies that store large amount of data. Recently, the challenges are to effectively utilize the incredible amount of data and to obtain knowledge that benefits business, scientific, and government transactions by using subset of features rather than the whole features in the dataset. In the present paper, we have focused on feature selection techniques as a method to gain high quality attributes to enhance the mining process. Feature selection techniques touch all disciplines that require knowledge discovery from large data. In our study, we made a comparison between benchmark feature selection methods based on three well-known datasets and three well-recognized machine learning algorithms. The study found that feature selection methods are capable to improve the performance of learning algorithms. However, no single feature selection methods that best satisfy all datasets and learning algorithms. Therefore, machine learning researchers should understand the nature of datasets and learning algorithms characteristics in order to obtain better outcome. Overall, correlation based feature selection (CFS) and consistency based subset evaluation (CB) performed better than information gain, symmetrical uncertainty, Relief (RF), and principle components analysis (PC).

Accurately classifying medical data is critical for improving diagnostic prediction system and identifying threptic targets for treatments. Analysing gene expression data has a major challenge in extracting disease-related genes from the large number of genes output from next generation sequencing technology. Therefore, eliminating irrelevant and redundant genes is a major step to process data for prediction. Our objective is to predict more accurately the presence of cancer disease in a sample cell from the gene expression.In this paper, we create a function called Classification Technique as Feature Selection (CTFS) as a new feature selection (FS) method to extract a subset (small number) of genes from classified big number of genes expression to improve cancer prediction result. The enrolled classification techniques in CTFS function for selection are K-Nearest Neighbors (K-NN) and Extreme Gradient Boosting (XGBoosting) optimized by Bayesian Parameter Tuning (BPT). The feature selection methods used to investigate the performance of CTFS function are Univariate Feature Selection (UFS) and Feature Importance (FI). The classification stage is carried out after the feature selection stage using three machine learning (ML) algorithms, Naïve Bayes (NB), Linear Support Vector Machine (LSVM), and Random Forest (RF). Results shows that, using XGBoosting optimized by BPT for FS outperforms FI method in terms of increasing the prediction accuracies along with minimum number of features but with higher running time. The performance of K-NN in FS outperforms all other FS methods in terms of accuracies providing an accuracy that is up to 100% when applied with LSVM on simulation dataset.

An unsupervised feature selection algorithm based on ant colony optimization

QSAR analysis of diaryl COX-2 inhibitors: Comparison of feature selection and train-test data selection methods

Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as the second deadliest cancer after lung cancer, can be highly beneficial. Moreover, the standard treatment for locally advanced colorectal cancer, which is widely accepted around the world, is chemoradiotherapy. Then, in this study, seven artificial intelligence models including decision tree, K-nearest neighbors, Adaboost, random forest, Gradient Boosting, multi-layer perceptron, and convolutional neural network were implemented to detect patients responder and non-responder to radiochemotherapy. For finding the potential predictors (genes), three feature selection strategies were employed including mutual information, F-classif, and Chi-Square. Based on feature selection models, four different scenarios were developed and five, ten, twenty and thirty features selected for designing a more accurate classification paradigm. The results of this study confirm that random forest, Gradient Boosting, decision tree, and K-nearest neighbors provided more accurate results in terms of accuracy, by 93.8%. Moreover, Among the feature selection methods, mutual information and F-classif showed the best results, while Chi-Square produced the worst results. Therefore, the suggested artificial intelligence models can be successfully applied as a robust approach for classification of colorectal cancer response to radiochemotherapy for medical studies.