Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data

Abstract

Data plays a key role in the design of expert and intelligent systems and therefore, data preprocessing appears to be a critical step to produce high-quality data and build accurate machine learning models. Over the past decades, increasing attention has been paid towards the issue of class imbalance and this is now a research hotspot in a variety of fields. Although the resampling methods, either by undersampling the majority class or by over-sampling the minority class, stand among the most powerful techniques to face this problem, their strengths and weaknesses have typically been discussed based only on the class imbalance ratio. However, several questions remain open and need further exploration. For instance, the subtle differences in performance between the over-and under-sampling algorithms are still under-comprehended, and we hypothesize that they could be better explained by analyzing the inner structure of the data sets. Consequently, this paper attempts to investigate and illustrate the effects of the resampling methods on the inner structure of a data set by exploiting local neighborhood information, identifying the sample types in both classes and analyzing their distribution in each resampled set. Experimental results indicate that the resampling methods that pro

Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

“…Over and under sampling strategies are very popular and effective approaches to deal with the class imbalance problem [21,25,33,50]. To compensate the class imbalance by biasing the process of discrimination, the ROS algorithm randomly replicates samples from the minority classes while the RUS technique randomly eliminates samples from the majority classes, until achieving a relative classes balance [23,60].…”

“…Hybrid methods generally employ SMOTE to compensate the class imbalance, because this method reduces the possibilities of over-training or over-fitting [1]. They use methods based in nearest neighbor rule to reduce overlap or noise in the dataset [25].…”

“…Recently, hybrid methods have increased in popularity in the machine learning community, as it can be observed in several references [23,25,66], which have studied SMOTE+TL, SMOTE+CNN and SMOTE+ENN, among others methods, to deal with class imbalance problem and to eliminate samples in the overlapping region, in order to improve the classifier performance.…”

“…SMOTE+TL combines SMOTE and TL [64], SMOTE+CNN performs SMOTE and then CNN [65]. Figure 1a describes the operation of these hybrid approaches, which have been widely applied to deal with the class imbalance problem [22,25,29,33,50,[63][64][65]67]. The effectiveness of an additional hybrid approach is studied in this work, in which the training dataset is cleaned or reduced, and after, the number of samples by class in the training dataset is balanced.…”

“…Nowadays, it is one the most popular data sampling methods [1] and it has motivated the development of other over-samplings algorithms [24]. Similarly, under-sampling methods have been incorporate a heuristic component [25], some of the most outstanding examples being the Tomek's Links (TL) [26], Editing Nearest Neighbor (ENN) [27], and Condensed Nearest Neighbor rule (CNN) [28], among others [22,[29][30][31][32][33].…”