基于相关性投票的轻量级特征选择方法，提升分类性能
HCVR: A Hybrid Approach with Correlation-aware Voting Rules for Feature Selection
- 结合参数间与参数-目标相关性，通过投票机制筛选特征
- 在SPAMBASE数据集上优于传统非迭代与迭代方法
- 适合需要高效降维且追求高分类精度的场景
本文提出HCVR（基于相关性感知投票规则的混合方法），一种轻量级基于规则的特征选择方法，通过融合参数间（P2P）与参数-目标（P2T）相关性，剔除冗余特征并保留相关特征。该方法结合非迭代与迭代过滤策略，采用贪心后向消除机制，每步可移除多个特征。基于相关性阈值设定投票规则，通过多数表决决定特征去留。在SPAMBASE数据集上的实验表明，相较于传统非迭代方法（如CFS、mRMR、MI）和迭代方法（如RFE、SFS、遗传算法），HCVR在不同分类器上的表现均有提升。
原文摘要 · Abstract (English)
In this paper, we propose HCVR (Hybrid approach with Correlation-aware Voting Rules), a lightweight rule-based feature selection method that combines Parameter-to-Parameter (P2P) and Parameter-to-Target (P2T) correlations to eliminate redundant features and retain relevant ones. This method is a hybrid of non-iterative and iterative filtering approaches for dimensionality reduction. It is a greedy method, which works by backward elimination, eliminating possibly multiple features at every step. The rules contribute to voting for features, and a decision to keep or discard is made by majority voting. The rules make use of correlation thresholds between every pair of features, and between features and the target. We provide the results from the application of HCVR to the SPAMBASE dataset. The results showed improvement performance as compared to traditional non-iterative (CFS, mRMR and MI) and iterative (RFE, SFS and Genetic Algorithm) techniques. The effectiveness was assessed based on the performance of different classifiers after applying filtering.
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