Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network
Masoumeh Soleimani, Zahra Forouzanfar, Morteza Soltani, Majid Jafari Harandi
Abstract
Data classification in the real world is often faced with the challenge of data imbalance, where there is asignificant difference in the number of instances among different classes. Dealing with imbalanced data isrecognized as a challenging problem in data mining, as it involves identifying minority-class data with ahigh number of errors. Therefore, the selection of unique and appropriate features for classifying data withsmaller classes poses a fundamental challenge in this research. Nowadays, due to the widespread presenceof imbalanced medical data in many real-world problems, the processing of such data has gained attentionfrom researchers. The objective of this research is to propose a method for classifying imbalanced medicaldata. In this paper, the hypothyroidism dataset from the UCI repository is used. In the feature selection stage,a support vector machine algorithm is used as a cost function, and the wrapper algorithm is employed asa search strategy to achieve an optimal subset of features. The proposed method achieves high accuracy,reaching 99.6% accuracy for data classification through the optimization of a neural network using learningautomata.