Abstract
Data classification is an important task in the field of data mining, which can be used to mine the model of important data and forecast the future trend of those data. Although some breakthroughs have been made in data classification theoretically and technically, there are still some problems, such as lack accuracy of classification modeling algorithm, poor comprehensibility of classification rules and so on. Accuracy improvement and accurate achievement of classification has become hot research topics. Gene expression programming (GEP) has been considered a powerful evolutionary method for data classification. Aiming at the shortage of basic GEP classification algorithm, a novel classification algorithm based on GEP named O_GEPCA has been proposed in this paper. By using this method the initialization and mutation operator adjustment method, calibration set, evolution function and correction strategy will be improved, and the basic GEP classification algorithm will be optimized. The proposed O_GEPCA method shows significantly improvement after comparative study between our proposed O_GEPCA methods and the primitive GEP. The efficiency and capability of our proposed O_GEPCA for data classification will be tested in four well-studied benchmark test cases including card, cancer, heart, glass classification problem demonstrate.
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Acknowledgements
This work was partially supported by Science and Technology Project of Guangdong Province of China (Grant Nos. 2017A020224004 and 2016A020212020), Science and Technology Key Project of Guangdong Province of China (Grant Nos. 2016B010110005 and 2014B020205004) and Fund of Natural Science Foundation of Guangdong Province of China (Grant No. 2015GA780062). The authors also gratefully acknowledge the reviewers for their helpful comments and suggestions that helped to improve the presentation.
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Yang, L., Li, K., Zhang, W. et al. Optimization of classification algorithm based on gene expression programming. J Ambient Intell Human Comput 15, 1261–1275 (2024). https://doi.org/10.1007/s12652-017-0563-8
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DOI: https://doi.org/10.1007/s12652-017-0563-8