Abstract
Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR’s performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced.
Highlights
IntroductionIn the settings of binary category, a dataset is called “imbalanced” if the number of one class is far larger than the others in the training data
Datasets with imbalanced distribution are quite common in classification
A natural way to combat this challenge may combine each filtering approach’s information and relieve the effect of class imbalance. This is the motivation for why we propose the strategy of rank aggregation with re-balance
Summary
In the settings of binary category, a dataset is called “imbalanced” if the number of one class is far larger than the others in the training data. The majority class is called negative while the minority class is called positive. A hindrance in class-imbalance learning is that standard classifiers are often biased towards the majority classes. Re-sampling is the standard strategy to deal with class-imbalance learning tasks. Many studies [2,3,4] have shown that re-sampling the dataset is an effective way to enhance the overall performance of the classification for several types of classifiers. Re-sampling methods concentrate on modifying the training set to make it suitable for a standard classifier. There are generally three types of re-sampling strategies to balance the class distribution: over-sampling, under-sampling, and hybrid sampling
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