Feature selection algorithm based on incremental mutual information and cockroach swarm optimization
Zhao, Chen

TL;DR
This paper introduces an improved swarm optimization algorithm based on incremental mutual information and rough set theory for efficient feature selection in high-dimensional, large-scale datasets, enhancing accuracy and reducing feature subset size.
Contribution
It proposes a novel IMIICSO method that combines incremental mutual information with cockroach swarm optimization, addressing computational challenges in high-dimensional feature selection.
Findings
Improved accuracy of feature subsets compared to baseline algorithms.
Reduced feature subset size while maintaining performance.
Validated effectiveness on 10 diverse datasets.
Abstract
Feature selection is an effective preprocessing technique to reduce data dimension. For feature selection, rough set theory provides many measures, among which mutual information is one of the most important attribute measures. However, mutual information based importance measures are computationally expensive and inaccurate, especially in hypersample instances, and it is undoubtedly a NP-hard problem in high-dimensional hyperhigh-dimensional data sets. Although many representative group intelligent algorithm feature selection strategies have been proposed so far to improve the accuracy, there is still a bottleneck when using these feature selection algorithms to process high-dimensional large-scale data sets, which consumes a lot of performance and is easy to select weakly correlated and redundant features. In this study, we propose an incremental mutual information based improved…
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Taxonomy
TopicsRough Sets and Fuzzy Logic · Data Mining Algorithms and Applications
MethodsFeature Selection · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
