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

Masoumeh Soleimani; Zahra  Forouzanfar; Morteza Soltani; Majid Jafari Harandi

doi:10.4108/airo.3526

Authors

Masoumeh Soleimani Clemson University
Zahra Forouzanfar Islamic Azad University, Isfahan
Morteza Soltani Clemson University
Majid Jafari Harandi Islamic Azad University of Khomeynishahr

DOI:

https://doi.org/10.4108/airo.3526

Keywords:

Classification, Imbalanced Data, Neural Network, Learning Automata

Abstract

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
automata.

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