Scale Coarsening as Feature Selection

To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855-0.916; sensitivity: 0.715-0.892; and specificity: 0.733-0.862) and the unseen test set (area under the curve: 0.751-0.826; sensitivity: 0.581-0.839; and specificity: 0.641-0.923). Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.

Feature selection in machine learning: A new perspective

Breast cancer is one of the significant tumor death in women. Computer-aided diagnosis (CAD) supports the radiologists in recognizing the irregularities in an efficient manner. In this work, a novel CAD system proposed for mammogram image analysis based on grey wolf optimizer (GWO) with rough set theory. Texture, intensity, and shape-based features are extracted from mass segmented mammogram images. To derive the appropriate features from the extracted feature set, a novel dimensionality reduction algorithm is proposed based on GWO with rough set theory. GWO is a novel bio-inspired optimization algorithm, stimulated based on hunting activities and social hierarchy of the grey wolves. In this paper, a hybridization of GWO and Rough Set (GWORS) methods are used to find the significant features from the extracted mammogram images. To evaluate the effectiveness of the proposed GWORS, we compare it with other well-known rough set and bio-inspired feature selection algorithms including particle swarm optimize, genetic algorithm, Quick Reduct and Relative Reduct. From empirical results, it is observed that the proposed GWORS outperforms the other techniques in terms of accuracy, F-Measures and receiver operating characteristic curve.

Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic monitoring may contain some correlated records due to temporal correlation or user correlation. To fill this gap, in this article, we propose a correlation reduction scheme with differentially private feature selection considering the issue of privacy loss when data have correlation in machine learning tasks. The proposed scheme involves five steps with the goal of managing the extent of data correlation, preserving the privacy, and supporting accuracy in the prediction results. In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover the privacy issue of data correlation in learning is guaranteed. The proposed method can be widely used in machine learning algorithms, which provide services in industrial areas. Experiments show that the proposed scheme can produce better prediction results with machine learning tasks and fewer mean square errors for data queries compared to existing schemes.

Human Action Recognition (HAR) is a vital part of the healthcare sector. Medical practitioners are experiencing difficulty in recognizing human physiological conditions due to the enormous quantity of sensory stimulation. Machine learning techniques help to predict human physiological conditions, providing medical practitioners to work more efficiently. Feature selection is an essential part of discovering new knowledge in the majority of real-world problems when there are a lot of features. Feature selection is extremely useful because it speeds up decisions and enhances classification performance. The importance of feature selection in machine learning is dimension reduction in a massive multivariate data collection. This paper presents an effective feature selection method known as the Enhanced Recursive Feature Elimination (EFRE) for selecting key features from the data set for HAR prediction. The experimental results reveal that the ERFE technique selects the most suitable features for HAR prediction. The performance of the proposed ERFE approach is tested using different performance evaluation metrics. The performance analysis shows that the ERFE method outperforms existing feature selection methods with 88% accuracy.

In many fields such as data mining, machine learning, pattern recognition and signal processing, data sets containing huge number of features are often involved. Feature selection is an essential data preprocessing technique for such high-dimensional data classification tasks. Traditional dimensionality reduction approach falls into two categories: Feature Extraction (FE) and Feature Selection (FS). Principal component analysis is an unsupervised linear FE method for projecting high-dimensional data into a low-dimensional space with minimum loss of information. It discovers the directions of maximal variances in the data. The Rough set approach to feature selection is used to discover the data dependencies and reduction in the number of attributes contained in a data set using the data alone, requiring no additional information. For selecting discriminative features from principal components, the Rough set theory can be applied jointly with PCA, which guarantees that the selected principal components will be the most adequate for classification. We call this method Rough PCA. The proposed method is successfully applied for choosing the principal features and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expressiondata.

A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

We present a method to learn maximal generalized decision rules from databases by integrating discretization, generalization and rough set feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated and the numeric attributes are discretized into a few intervals. The primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. Horizontal reduction is accomplished by merging identical tuples after the substitution of an attribute value by its higher level value in a pre‐defined concept hierarchy for symbolic attributes, or the discretization of continuous (or numeric) attributes. This phase greatly decreases the number of tuples in the database. In the second phase, a novel context‐sensitive feature merit measure is used to rank the features, a subset of relevant attributes is chosen based on rough set theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without destroying the interdependence relationships between classes and the attributes. Then rough set‐based value reduction is further performed on the reduced table and all redundant condition values are dropped. Finally, tuples in the reduced table are transformed into a set of maximal generalized decision rules. The experimental results on UCI data sets and a real market database demonstrate that our method can dramatically reduce the feature space and improve learning accuracy.

Feature selection based on rough sets and particle swarm optimization

Positive approximation: An accelerator for attribute reduction in rough set theory

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.

In machine learning, Case Based Reasoning is a prominent technique for harvesting knowledge from past experiences. The past experiences are represented in the form of a repository of cases having a set of features. But each feature may not have the equal relevancy in describing a case. Measuring the relevancy of each feature is always a prime issue. A subset of relevant features describes a case with adequate accuracy. An appropriate subset of relevant features should be selected for improving the performance of the system and to reduce dimensionality. In case based domain, feature selection is a process of selecting an appropriate subset of relevant features. There are various real domains which are inherently case based and features are expressed in terms of linguistic variables. To assign a numerical weight to each linguistic feature, a lot of feature subset selection algorithms have been proposed. But the weighting values are usually determined using subjective judgement or a trial and error basis.

Type 1 Diabetes Mellitus (T1D) has been increasingly associated with alterations in the gut microbiome. However, the impact of taxonomic resolution, feature selection strategies, and machine learning methods on microbiome-based prediction remains incompletely understood. We analyzed publicly available 16S rRNA gene sequencing datasets from two geographic cohorts to evaluate microbiome-based prediction of T1D. Microbial features were constructed at multiple taxonomic levels and as full hierarchical taxonomic paths preserving phylogenetic structure. Machine learning models were trained using stratified cross-validation and cross-cohort validation frameworks. Feature selection was performed using Binary Particle Swarm Optimization (BPSO) to identify compact and predictive microbial signatures. Model performance was evaluated using AUC, Accuracy, F1 score, and Matthews Correlation Coefficient. Differential abundance analysis using the LinDA framework was used to support biological interpretation of selected taxa. Tree-based models, particularly Random Forest and XGBoost, achieved the strongest predictive performance across taxonomic representations. Taxonomic resolution influenced model behavior, with family-level features providing strong performance with compact feature sets, while higher-resolution representations did not consistently improve performance despite increased complexity. BPSO identified consistently selected taxa across validation frameworks, suggesting stable predictive signatures. Several of these taxa have been linked to inflammatory or metabolically altered gut environments. Cross-cohort validation showed reduced performance compared with within-study models, highlighting challenges in generalization. Machine learning combined with BPSO-based feature selection provides an effective framework for identifying predictive microbial signatures associated with T1D. Our findings highlight the importance of taxonomic resolution, feature stability, and cross-cohort validation in microbiome-based predictive modeling. Integrating evolutionary feature selection with machine learning and biological validation may improve the robustness and interpretability of candidate microbial signatures.

Alzheimer’s disease is a progressive neurodegenerative disorder for which early detection remains a significant challenge due to the complexity of clinical features and the high dimensionality of medical data. This study aims to improve the accuracy and reliability of Alzheimer’s disease detection by evaluating the performance of multiple machine learning algorithms integrated with intelligent feature selection strategies. Five classification models, Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, and Deep Learning, were investigated under two experimental scenarios: without feature selection and with feature selection using Recursive Feature Elimination, Binary Particle Swarm Optimization, and Variance Threshold. Model performance was evaluated using K-fold cross-validation based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that feature selection consistently enhances classification performance, particularly for conventional machine learning models such as Random Forest and Logistic Regression. Although the Deep Learning model achieves competitive accuracy, its reduced precision and F1-score indicate limitations when applied to reduced feature spaces. These findings highlight the importance of incorporating appropriate feature selection techniques to address data complexity and improve the effectiveness of early Alzheimer’s disease detection.

Feature selection plays an important role in the fields of pattern recognition, data mining and machine learning. Rough set method is one of effective methods for feature selection, which can preserve the meaning of the features. Presently ant colony optimization (ACO) has been successfully applied to rough set-based feature selection, however, it has the limitations of many control parameters, premature convergence and stagnation. In this paper, we propose a novel discrete artificial bee colony algorithm to overcome these drawbacks. In our algorithm, a search mechanism centered with feature core is introduced to avoid blindly searching, in which the values of corresponding bits of these features in feature core are unchanged during the iterative process. Meanwhile, Tabu search is adopted to avoid falling into local optima for every employed bee who can not select the solution of the Tabu list in each generation. In this work, our algorithm is applied to eight datasets from UCI database and the results produced by our algorithm and some rough set-based feature selection algorithms have been compared. The results show that our algorithm outperforms the other algorithms.