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arXiv · 2210.12446

Learning Classifiers for Imbalanced and Overlapping Data

Shivaditya Shivganesh·Nitin Narayanan N·Pranav Murali·Ajaykumar M
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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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Shivaditya Shivganesh, Nitin Narayanan N, Pranav Murali, Ajaykumar M. 2022-10-22. Learning Classifiers for Imbalanced and Overlapping Data. https://arxiv.org/abs/2210.12446

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