Decision trees are an efficient data analysis tool. Ensembling methods have been developed on the basis of decision trees. These methods make it possible to obtain a data analysis tool in the form of a composition of trees. The paper proposes a new approach since the development of compositions based on decision trees is an urgent problem. The paper proposes a new hybrid approach to designing the composition of decision trees. The approach is based on the idea of the decision tree application built by a genetic programming algorithm as a technique to determine a machine learning method for object classification. Thus, with the help of the proposed approach the authors carry out a hybridization of a self-configuring genetic programming algorithm and a decision tree. The paper treats decision trees built by a modified algorithm with differential evolution considered as data analysis methods that make decisions concerning a sample objects classification. The proposed method is studied on some classification problems with different types of data and dimensions. The comparison with other methods for building compositions of decision trees is made.
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27 February 2023
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Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
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Mitrofanov, S. A., & Karaseva, T. S. (2023). Hybrid Evolutionary Approach to Decision Trees Ensembles Design. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 111-116). European Publisher. https://doi.org/10.15405/epct.23021.14