Correlation-Based and Causal Feature Selection Analysis for Ensemble Classifiers

摘要

High dimensional feature spaces with relatively few samples usually leads to poor classifier performance for machine learning, neural networks and data mining systems. This paper presents a comparison analysis between correlation-based and causal feature selection for ensemble classifiers. MLP and SVM are used as base classifier and compared with Naive Bayes and Decision Tree. According to the results, correlation-based feature selection algorithm can eliminate more redundant and irrelevant features, provides slightly better accuracy and less complexity than causal feature selection. Ensemble using Bagging algorithm can improve accuracy in both correlation-based and causal feature selection.