Learning Classifiers for Imbalanced and Overlapping Data

TL;DR

This paper investigates methods for improving classifiers on imbalanced and overlapping data, introducing a new sparsity-based approach and comparing various resampling techniques on artificial datasets.

Contribution

It presents a novel sparsity method to enhance classifier performance and evaluates multiple resampling strategies on artificially generated imbalanced datasets.

Findings

Sparsity improves classifier accuracy on imbalanced data.

Resampling methods like over-sampling and NCR under-sampling show varying effectiveness.

Artificial datasets help analyze data characteristics affecting classification.

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.