Advisor(s) - Committee Chair
Huanjing Wang (Director), Guangming Xing and Zhonghang Xia
Department of Computer Science
Master of Science
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.
Bioinformatics | Computer Sciences | Databases and Information Systems
Pan, Yi, "A Proposed Frequency-Based Feature Selection Method for Cancer Classification" (2017). Masters Theses & Specialist Projects. Paper 1954.