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.

翻译：本文提出期望置信依赖（ECD），这是一种新颖的、面向软计算的、以精度驱动的依赖度量方法，用于粗糙集理论框架内的特征选择。与依赖条件块二元表征的传统粗糙集依赖度量不同，ECD为各个等价块分配基于置信度的贡献，并通过归一化期望算子进行聚合。我们正式确立了ECD的若干理想性质，包括归一化性、与经典依赖的兼容性、单调性，以及在结构和标签保持变换下的不变性。