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|Title||Classifying Muti-Class Imbalance Data|
Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a minority class. In this case, standard classifiers tend to classify all examples as a majority class and completely ignore the minority class. For this problem, researchers proposed some solutions at both data and algorithmic levels. Most efforts concentrate on binary class problems. However, binary class is not the only scenario where the class imbalance problem prevails. In the case of multi-class data sets, it is much more difficult to define the majority and minority classes. Hence, multi class classification in imbalanced data sets remains an important topic of research. In our research, we proposed new approach based on SOMTE (Synthetic Minority Over-sampling TEchnique) and clustering which is able to deal with imbalanced data problem involving multiple classes. We implemented our approach and experimental results show our approach is effective to deal with the multi class imbalanced data sets, and can improve the classification performance of minority class and its performance on the whole data set. In the best case, our F-measure improved from 66.91 to 95.18.
|Published in||Egyptian Computer Science Journal|
|Series||Volume: 37, Number: 5|
|Item link||Item Link|
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|El-Halees, Alaa M._33.pdf||480.0Kb|