Imbalanced Datasets: Towards a better classification using boosting methods

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.

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

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