Imbalanced Data Classification: A Novel Re-sampling Approach Combining Versatile Improved SMOTE and Rough Sets
In recent years, the problem of learning from imbalanced data has emerged as important and challenging. The fact that one of the classes is underrepresented in the data set is not the only reason of difficulties. The complex distribution of data, especial
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Introduction
Proper classification of imbalanced data is one of the most challenging problems in data mining. Since wide range of real-world domains suffers from this issue, it is crucial to find more and more effective techniques to deal with it. The fundamental reason of difficulties is the fact that one class (positive, minority) is underrepresented in the data set. Furthermore, the correct recognition of examples belonging to this particular class is a matter of major interest. Considering domains like medical diagnostic, anomaly detection, fault diagnosis, detection of oil spills, risk management and fraud detection [8,21] the misclassification cost of rare cases is obviously very high. The small subset of data describing disease cases is more meaningful than remaining majority of objects representing healthy population. Therefore, the dedicated algorithms should be applied to recognizing minority class instances in these areas. c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved K. Saeed and W. Homenda (Eds.): CISIM 2016, LNCS 9842, pp. 31–42, 2016. DOI: 10.1007/978-3-319-45378-1 4
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K. Borowska and J. Stepaniuk
Over the last years the researchers’ growing interest in imbalanced data contributed to considerable advancements in this field. Numerous methods were proposed to address this problem. They are grouped into three main categories [8,21]: – data-level techniques: adding the preliminary step of data processing - assumes mainly undersampling and oversampling, – algorithm-level approaches: modifications of existing algorithms, – cost-sensitive methods: combining data-level and algorithm-level techniques to set different misclassification costs. In this paper we focus on data-level approaches: generating new minority class samples (oversampling) and introducing additional cleaning step (undersampling). Creating new examples of the minority class requires careful analysis of the data distribution. Random replication of the positive instances may lead to overfitting [8]. Furthermore, even applying methods like Synthetic Minority Oversampling Technique [5] (creation of new samples by interpolating several minority class examples that lie together) may not be sufficient for variety of real-life domains. Indeed, the main reason of difficulties in learning from imbalanced data is the complex distribution: existence of class overlapping, noise or small disjuncts [8,11,13,15]. The VIS algorithm [4], incorporated into the proposed approach, addresses listed problems by applying dedicated mechanism for each specific group of minority class examples. Assigning objects into categories is based on their local characteristics. Although this solution considers additional difficulties, in case of eminently complex problems it may contribute to creation of noisy objects. Hence, the clearing mechanism is introduced as the second step of preprocessing. On the other hand, new preliminary step deals with uncertainty by relabeling ambiguo
