Improving Imbalanced data classification accuracy by using Fuzzy Similarity Measure and subtractive clustering
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Abstract:
Classification is an one of the important parts of data mining and knowledge discovery. In most cases, the data that is utilized to used to training the clusters is not well distributed. This inappropriate distribution occurs when one class has a large number of samples but while the number of other class samples is naturally inherently low. In general, the methods of solving this kind of problem are divided into two categories: under-sampling and over-sampling. In this paper, an under-sampling method using subtractive clustering and fuzzy similarity measure was will be presented and their performances were are analyzed in terms of efficiency in classifying imbalanced data. For this purpose, the subtractive clustering is first conducted and the majority class data is clustered. Then, using fuzzy similarity measure, samples of each cluster were will be ranked and appropriate samples were are selected based on these rankings. The selected samples together with the minority class constituted create the final dataset. In this research, MATLAB software is used for implementation, the results are evaluation evaluated by using AUC criterion and analyzing results performed by using standard statistical. The experimental results show the effectiveness of proposed method to other methods of under-sampling.
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Journal title
volume 19 issue 2
pages 27- 38
publication date 2022-09
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