Imbalanced Datasets: Towards a better classification using boosting methods

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Date

2022-01-24

Journal Title

Journal ISSN

Volume Title

Publisher

University of Eloued جامعة الوادي

Abstract

Imbalanced datasets classification is inherently difficult. This situation becomes a challenge when amounts of data are processed to extract knowledge because traditional learning models fail to generate required results due to imbalanced nature of data. In this paper, we will address the problem of imbalanced datasets whether at the class level, or at the classifier level. In our work, we are interested in binary or multi-class classification. To do this, we present a set of techniques used to solve this problem in particular boosting methods and machine learning algorithms. Our goal is therefore to re-balance the dataset at the class level and to find an optimal classifier to handle these datasets after balancing. Through the results obtained, it was observed that the boosting methods are well suited to re-balance the data and thus give a very satisfactory classification result.

Description

Forum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloued

Keywords

Imbalanced Datasets; Supervised Classification; Boosting approach;Data Sampling Approach; SMOTEBoost Algorithm; RUSBoost Algorithm.

Citation

DJAFRI, Laouni. BACHA, Soufiane. Imbalanced Datasets: Towards a better classification using boosting methods. 24-26 Jan 2022. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here]