ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (11): 2613-2622.doi: 10.7544/issn1000-1239.2016.20150593

Previous Articles     Next Articles

Under-Sampling Method Based on Sample Weight for Imbalanced Data

Xiong Bingyan, Wang Guoyin, Deng Weibin   

  1. (Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065)
  • Online:2016-11-01

Abstract: Imbalanced data exists widely in the real world, and its classification is a hot topic in data mining and machine learning. Under-sampling is a widely used method in dealing imbalanced data set and its main idea is choosing a subset of majority class to make the data set balanced. However, some useful majority class information may be lost. In order to solve the problem, an under-sampling method based on sample weight for imbalance problem is proposed, named as KAcBag (K-means AdaCost bagging). In this method, sample weight is introduced to reveal the area where the sample is located. Firstly, according to the sample scale, a weight is made for each sample and is modified after clustering the data set. The samples which have less weight in the center of majority class. Then some samples are drawn from majority class in accordance with the sample weight. In the procedure, the samples in the center of majority class can be selected easily. The sampled majority class samples and all the minority class samples are combined as the training data set for a component classifier. After that, we can get several decision tree sub-classifiers. Finally, the prediction model is constructed based on the accuracy of each sub-classifiers. Experimental tests on nineteen UCI data sets and telecom user data show that KAcBag can make the selected samples have more representativeness. Based on that, this method can improve the the classification performance of minority class and reduce the scale of the problem.

Key words: imbalanced data, under-sampling, sample weight, clustering, ensemble learning

CLC Number: