چکیده مقاله
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
کلیدواژهها
نویسندگان
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;
شیوه ارجاع
Sabzekar, Mostafa and Deldari, Arash,1402,Imbalanced Data Classification Using Combination of Oversampling and Fuzzy Support Vector Machines,5th International Conference on Software Computing,Rudsar
ارائهشده در
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