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A novel, probability-based boolean feature selection algorithm
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DOI：10.1016/j.neucom.2026.133804.png)
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Abstract
En 中文
Boolean feature spaces are prevalent in prominent domains such as spam detection, disease prediction, and sentiment analysis, yet their high dimensionality often limits classification accuracy and computational efficiency. To address these challenges, this paper introduces the Probability-Based Boolean Feature Selection (PBFS) algorithm, which adopts a step-wise, condition-based approach for fast feature selection, designed to evaluate Boolean features through variance and probabilistic class-dependent frequency measures. Across 29 test cases, PBFS achieves the highest number of top accuracy scores and the best mean rank among all evaluated dimensionality reduction methods. It significantly reduces classifier runtime while maintaining one of the lowest feature selection execution times. The 239 experimental outcomes highlight PBFS as a competitive, scalable, and computationally efficient feature selection method for high-dimensional Boolean data, offering a new perspective on feature relevance assessment that supports effective classification in complex domains.
Keywords:
Feature selection
Boolean classification
Dimensionality reduction
Machine learning
Computational efficiency
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