Effective Methods of Categorical Data Encoding for Artificial Intelligence Algorithms

Furkat Bolikulov, Rashid Nasimov, Akbar Rashidov, Farkhod Akhmedov () and Young-Im Cho ()
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Furkat Bolikulov: Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
Rashid Nasimov: Department of Information Systems and Technologies, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
Akbar Rashidov: Department of Artificial Intelligence and Information Systems, Samarkand State University Named after Sharof Rashidov, Samarkand 140100, Uzbekistan
Farkhod Akhmedov: Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea
Young-Im Cho: Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea

Mathematics, 2024, vol. 12, issue 16, 1-22

Abstract: It is known that artificial intelligence algorithms are based on calculations performed using various mathematical operations. In order for these calculation processes to be carried out correctly, some types of data cannot be fed directly into the algorithms. In other words, numerical data should be input to these algorithms, but not all data in datasets collected for artificial intelligence algorithms are always numerical. These data may not be quantitative but may be important for the study under consideration. That is, these data cannot be thrown away. In such a case, it is necessary to transfer categorical data to numeric type. In this research work, 14 encoding methods of transforming of categorical data were considered. At the same time, conclusions are given about the general conditions of using these methods. During the research, categorical data in the dataset that were collected in order to assess whether it is possible to give credit to customers will be transformed based on 14 methods. After applying each encoding method, experimental tests are conducted based on the classification algorithm, and they are evaluated. At the end of the study, the results of the experimental tests are discussed and research conclusions are presented.

Keywords: artificial intelligence; data preprocessing; encoding methods; logistic classification algorithm; classification assessment methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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