TL;DR
A multi-tree and multi-strategy iterative genetic programming algorithm is proposed for feature subset construction and classification that attains higher average balanced classification accuracy than all baselines on nine datasets and surpasses them in macro F1-score on ten datasets.
In gene expression analysis, high dimension low sample size data limits the applicability of deep learning, motivating increasing interest in variable-length evolutionary algorithm–based feature selection methods (VLEAs). However, existing VLEAs suffer from unreliable single-metric discrimination under dynamic search-s...
A hybrid GA that integrates an information-theoretic local search strategy for unsupervised FS and effectively mitigates the premature convergence problem of conventional GAs and improves search efficiency and solution quality compared to traditional heuristic and evolutionary approaches is proposed.
Text classification is an important task in natural language processing that faces the main challenge of high dimensionality in the representation of text features, such as Bag-of-Words and TF-IDF. High dimensionality leads to data sparsity, increased computational complexity, and decreased model performance due to irr...
We introduce a robust classification model designed for feature selection. Support vector machine (SVM) models continue to play a crucial role in binary classification, particularly with tabular data. Their robust variants are essential for developing classifiers that remain stable despite shifts in data distribution...
Multi-objective feature selection provides an effective framework for high-dimensional data analysis by jointly considering classification performance and feature subset size. However, redundant and irrelevant features may degrade the quality of the initial search distribution, and population aggregation may reduce the...
INTRODUCTION Gene feature selection is essential in bioinformatics and medical research, as it identifies gene subsets closely associated with specific diseases or biological traits from high-dimensional gene datasets. High-dimensional gene data causes the curse of dimensionality, leading to sparsity, complex inter-fea...
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