Relevant and Redundant Feature Analysis with Ensemble Classification

— Feature selection and ensemble classification increase system efficiency and accuracy in machine learning, data mining and biomedical informatics. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using two datasets from UCI machine learning repository. Accuracy and computational time were evaluated by four base classifiers; NaiveBayes, Multilayer Perceptron, Support Vector Machines and Decision Tree. Eliminating irrelevant features improves accuracy and reduces computational time while removing redundant features reduces computational time and reduces accuracy of the ensemble. Keywords- Feature selection; Ensemble classification; redundant feature; irrelevant feature

• Type: Conference
• Year: 2009
• Where: ICAPR
• Authors: Rakkrit Duangsoithong, Terry Windeatt
• Keywords: Computational Time, Feature Selection, ICAPR 2009, Pattern Recognition, Redundant Feature