Publication Date

5-2013

Advisor(s) - Committee Chair

Huanjing Wang (Director), Qi Li, Rong Yang

Degree Program

Department of Computer Science

Degree Type

Master of Science

Abstract

Feature selection is one of the important data preprocessing steps in data mining. The feature selection problem involves finding a feature subset such that a classification model built only with this subset would have better predictive accuracy than model built with a complete set of features. In this study, we propose two hybrid methods for feature selection. The best features are selected through either the hybrid methods or existing feature selection methods. Next, the reduced dataset is used to build classification models using five classifiers. The classification accuracy was evaluated in terms of the area under the Receiver Operating Characteristic (ROC) curve (AUC) performance metric. The proposed methods have been shown empirically to improve the performance of existing feature selection methods.

Disciplines

Artificial Intelligence and Robotics | Computer Sciences | Databases and Information Systems

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