Expected Confidence Dependency: A Novel Rough Set-Based Approach to Feature Selection

Saeed Rasouli; Hamid Karamikabir

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arXiv · 2512.03612

Expected Confidence Dependency: A Novel Rough Set-Based Approach to Feature Selection

Saeed Rasouli·Hamid Karamikabir

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Abstract

This paper proposes Expected Confidence Dependency (ECD), a novel, soft computing-oriented, accuracy driven dependency measure for feature selection within the rough set theory framework. Unlike traditional rough set dependency measures that rely on binary characterizations of conditional blocks, ECD assigns confidence-based contributions to individual equivalence blocks and aggregates them through a normalized expectation operator. We formally establish several desirable properties of ECD, including normalization, compatibility with classical dependency, monotonicity, and invariance under structural and label-preserving transformations.

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Saeed Rasouli, Hamid Karamikabir. 2025-12-03. Expected Confidence Dependency: A Novel Rough Set-Based Approach to Feature Selection. https://arxiv.org/abs/2512.03612

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