MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification

TL;DR

This paper introduces MEBoost, a novel boosting algorithm that combines different weak learners to effectively address class imbalance in datasets, outperforming existing methods in multiple benchmark tests.

Contribution

MEBoost is a new boosting approach that mixes two weak learners to improve classification of imbalanced datasets, providing an effective alternative to existing techniques.

Findings

MEBoost outperforms state-of-the-art ensemble methods on benchmark datasets.

Experimental results show significant accuracy improvements.

MEBoost is a promising method for imbalanced data classification.

Abstract

Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machine learning algorithms try to maximize the accuracy classification by correctly identifying majority class samples while ignoring the minority class. However, the concept of the minority class instances usually represents a higher interest than the majority class. Recently, several cost sensitive methods, ensemble models and sampling techniques have been used in literature in order to classify imbalance datasets. In this paper, we propose MEBoost, a new boosting algorithm for imbalanced datasets. MEBoost mixes two different weak learners with boosting to improve the performance on imbalanced datasets. MEBoost is an alternative to the existing techniques such as SMOTEBoost, RUSBoost, Adaboost, etc. The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost. Experimental results show significant improvement over the other methods and it can be concluded that MEBoost is an effective and promising algorithm to deal with imbalance datasets. The python version of the code is available here: https://github.com/farshidrayhanuiu/