Publication Date

Spring 2017

Advisor(s) - Committee Chair

Huanjing Wang (Director), Guangming Xing and Zhonghang Xia

Degree Program

Department of Computer Science

Degree Type

Master of Science

Abstract

Feature selection method is becoming an essential procedure in data preprocessing step. The feature selection problem can affect the efficiency and accuracy of classification models. Therefore, it also relates to whether a classification model can have a reliable performance. In this study, we compared an original feature selection method and a proposed frequency-based feature selection method with four classification models and three filter-based ranking techniques using a cancer dataset. The proposed method was implemented in WEKA which is an open source software. The performance is evaluated by two evaluation methods: Recall and Receiver Operating Characteristic (ROC). Finally, we found the frequency-based feature selection method performed better than the original ranking method.

Disciplines

Bioinformatics | Computer Sciences | Databases and Information Systems

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