HCVR: A Hybrid Approach with Correlation-aware Voting Rules for Feature Selection
This is an incremental improvement for feature selection in machine learning, potentially benefiting data preprocessing tasks.
The paper tackled feature selection by proposing HCVR, a hybrid rule-based method that uses correlation-aware voting to eliminate redundant features, and demonstrated improved performance over traditional techniques on the SPAMBASE dataset.
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.