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Activity Number: 659 - Recent Advances in Dimension Reduction and Clustering
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #302972
Title: Dimension Reduction and Classification of Imbalanced Data
Author(s): Elizabeth Chou*
Companies: National Chengchi University
Keywords: imbalanced data; dimension reduction; semisupervised learning; supervised learning; classification

The two most challenging studies in machine learning are dimension reduction and imbalanced data analysis. However, few studies have been made to develop in dimension reduction techniques for imbalanced data. The current study tries to solve these two challenges. A dimension reduction method for imbalanced data is proposed. We try to narrow down the numbers of features and find a dual-relationship between the small subset of features and the minority class. We also test the performance of the proposed dimension reduction method on some real-world datasets. The results show that the hidden inter-dependence patterns are discovered through the proposed method to improve classification accuracy.

Authors who are presenting talks have a * after their name.

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