Imbalanced Data Classification Using Combination of Oversampling and Fuzzy Support Vector Machines
Publish place: 5th International Conference on Software Computing
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG05_029
تاریخ نمایه سازی: 9 اردیبهشت 1403
Abstract:
Classifying imbalanced data stands as a critical aspect in machine learning, posing substantial hurdles due to the uneven distribution of data. Diverse methods have emerged to address such challenges in data categorization. This study aims to alleviate data imbalances while leveraging Fuzzy Support Vector Machines (FSVM) to bolster resilience against noisy and outlier data in mining tasks. Initially, our approach involves preprocessing the data via the SMOTE algorithm to establish a balanced dataset. This algorithm synthesizes data for the minority class by considering the proximity of individual samples. Following this, we employ Fuzzy Support Vector Machines to classify the preprocessed data. Lastly, we introduce a novel membership function for FSVM. The UCI dataset serves as the testing ground. Comparative results showcase the proposed method's adeptness in effectively handling imbalanced data.
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Authors
Mostafa Sabzekar
Assistant Professor, Department of Computer Engineering, Birjand University of Technology, Birjand, Iran;
Arash Deldari
Assistant Professor, Department of Computer Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran;