LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification

TL;DR

LIUBoost is a novel boosting method that addresses class imbalance by combining undersampling with instance hardness-based cost adjustments, leading to improved classification performance on imbalanced datasets.

Contribution

Introduces LIUBoost, a new boosting algorithm that integrates instance hardness into undersampling to enhance imbalanced data classification.

Findings

Significantly outperforms RUSBoost on 18 datasets.

Effectively reduces information loss during undersampling.

Improves classification accuracy in imbalanced scenarios.

Abstract

The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinality of the classes under consideration while optimizing the cost function and this assumption does not hold true for imbalanced datasets which results in sub-optimal classification. Therefore, various approaches, such as undersampling, oversampling, cost-sensitive learning and ensemble based methods have been proposed for dealing with imbalanced datasets. However, undersampling suffers from information loss, oversampling suffers from increased runtime and potential overfitting while cost-sensitive methods suffer due to inadequately defined cost assignment schemes. In this paper, we propose a novel boosting based method called LIUBoost. LIUBoost uses under sampling for balancing the datasets in every boosting iteration like RUSBoost while incorporating a cost term for every instance based on their hardness into the weight update formula minimizing the information loss introduced by undersampling. LIUBoost has been extensively evaluated on 18 imbalanced datasets and the results indicate significant improvement over existing best performing method RUSBoost.