Learning Classifiers for Imbalanced and Overlapping Data

• URL: http://arxiv.org/abs/2210.12446v1
• Date: Sat, 22 Oct 2022 13:31:38 GMT
• Title: Learning Classifiers for Imbalanced and Overlapping Data
• Authors: Shivaditya Shivganesh, Nitin Narayanan N, Pranav Murali, Ajaykumar M
• Abstract summary: This study is about inducing classifiers using data that is imbalanced. A minority class is under-represented in relation to the majority classes. This paper further optimises class imbalance with a new method called Sparsity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study is about inducing classifiers using data that is imbalanced, with a minority class being under-represented in relation to the majority classes. The first section of this research focuses on the main characteristics of data that generate this problem. Following a study of previous, relevant research, a variety of artificial, imbalanced data sets influenced by important elements were created. These data sets were used to create decision trees and rule-based classifiers. The second section of this research looks into how to improve classifiers by pre-processing data with resampling approaches. The results of the following trials are compared to the performance of distinct pre-processing re-sampling methods: two variants of random over-sampling and focused under-sampling NCR. This paper further optimises class imbalance with a new method called Sparsity. The data is made more sparse from its class centers, hence making it more homogenous.

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