Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network




Classification, Imbalanced Data, Neural Network, Learning Automata


Data classification in the real world is often faced with the challenge of data imbalance, where there is a
significant difference in the number of instances among different classes. Dealing with imbalanced data is
recognized as a challenging problem in data mining, as it involves identifying minority-class data with a
high number of errors. Therefore, the selection of unique and appropriate features for classifying data with
smaller classes poses a fundamental challenge in this research. Nowadays, due to the widespread presence
of imbalanced medical data in many real-world problems, the processing of such data has gained attention
from researchers. The objective of this research is to propose a method for classifying imbalanced medical
data. 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 as
a 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 learning


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How to Cite

M. Soleimani, Z. Forouzanfar, M. Soltani, and M. Jafari Harandi, “Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network”, EAI Endorsed Trans AI Robotics, vol. 2, Jul. 2023.