The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Decision tree rule-based feature selection for imbalanced data
Author:	Liu, Haoyue
View Online:	njit-etd2017-076 (xi, 67 pages ~ 10.6 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Master of Science
Program:	Computer Engineering
Document Type:	Thesis
Advisory Committee:	Zhou, MengChu (Committee chair) Simeone, Osvaldo (Committee member) Shi, Yun Q. (Committee member)
Date:	2017-05
Keywords:	Imbalanced data Decision tree
Availability:	Unrestricted
Abstract:	A class imbalance problem appears in many real world applications, e.g., fault diagnosis, text categorization and fraud detection. When dealing with an imbalanced dataset, feature selection becomes an important issue. To address it, this work proposes a feature selection method that is based on a decision tree rule and weighted Gini index. The effectiveness of the proposed methods is verified by classifying a dataset from Santander Bank and two datasets from UCI machine learning repository. The results show that our methods can achieve higher Area Under the Curve (AUC) and F-measure. We also compare them with filter-based feature selection approaches, i.e., Chi-Square and F-statistic. The results show that they outperform them but need slightly more computational efforts.