CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification

Abstract

Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets requires employing some methods to compact the problem. In this paper, we propose a new hybrid approach aiming at reducing the dominance of the majority class instances using class decomposition and increasing the minority class instances using an oversampling method. Unlike other undersampling methods, which suffer data loss, our method preserves the majority class instances, yet significantly reduces its dominance, resulting in a more balanced dataset and hence improving the results. A large-scale experiment using 60 public datasets was carried out to validate the proposed methods. The results across three standard evaluation metrics show the comparable and superior results with other common and state-of-the-art techniques.

Class-Decomposition and Augmentation for Imbalanced Data Sentiment Analysis

“…The original CDSMOTE method presented in [10] is comprised of two steps: 1) class decomposition to redistribute the number of samples per class without losing any sample and 2) oversampling the new minority class(es) to reduce the dominance of the new majority class(es). Regarding the first step, class decomposition can be broadly described as the process of clustering class-instances into smaller groups by means of unsupervised learning algorithms.…”

“…In [22], an evolutionarybased method namely Genetic Algorithm was used to optimise a set of parameters including the best k values, and again an improved classification accuracy was achieved when the proposed method was tested on 22 different life science and medical datasets. More recently, class-decomposition was successfully applied to handle class-imbalance across various public and common imbalanced binary datasets [10]. The authors applied class-decomposition to reduce the dominance of the majority class instances, to then oversample the minority class instances.…”

“…Then, we apply k-means clustering to D P , which results in the samples of D P being mapped to a new set of classes P , where P = {p 1 , ..., p k }, being k the number of clusters selected for k in advance. Previous experiments in [10] showed that k values between 2 and 5 are optimal, provided that the imbalance ratio between majority and minority classes is not too high (i.e. IR > 50).…”

“…In a binary dataset, the problem happens when the distribution of the two classes is hugely imbalanced, which often leads learning algorithms to be biased toward the majority classinstances. In most literature, rare instances in the dataset are often referred to as the positive instance or the class of interest, while majority class instances are often referred to as negative instances [10].…”

“…A more recent work that used SMOTE was presented in [10]. Here, the authors, proposed a new method called CDSMOTE based on SMOTE and class-decomposition [22], [23].…”