Imbalanced data classification

This paper presents a data classification problem and methods to improve imbalanced data classification. Especially, biomedical data has a very high imbalance rate and the sample identification of minority class is a very important. Many studies have shown that border elements are important in imbalanced data classification such as Borderline-SMOTE, Random Under Border Sampling.

10p tamynhan9 02-12-2020 15 2   Download

In this paper, in order to increase the accuracy of the prediction model in imbalanced data classification problem, we propose a new cluster-based sampling method to address this work. Performing tests on a number of datasets, we have achieved important results when compared to cases without using any data balancing strategies and previous method.

9p koxih_kothogmih5 04-09-2020 25 3   Download

In this paper, we present an overview of the imbalanced data classification and the difficulties encountered in current approaches, from which we propose a new method, SMOTE-PLS. To evaluate the effectiveness of this new method, we conducted experiments based on standard cancer data sets from UCI sources, including breast-p, coil2000, leukemia, colon-cancer, and yeast.

9p koxih_kothogmih5 04-09-2020 4 1   Download