Binary Decision Tree Construction using the Hybrid Swarm Intelligence

Solving the problem of a classification model construction is presented in the form of a sequence of considered attributes and values thereof included in the M_k route from the root to the dangling vertex. Decision tree developed interpretation is presented as a pair of chromosomes (S_k, W_k). The S_k chromosome list of genes corresponds to the list of all attributes included in the M_k route in the decision tree. The W_k chromosome gene values correspond to the attribute values included in the M_k route. Unification of data structures, search space and modernization of integrable algorithms was carried out for hybridization. Hybrid algorithm operators are using the integer parameters and synthesize new integer parameter values. Method was developed to account for simultaneous attraction of the α_i particle to three x_i (t), x*_i (t), x*(t) attractors dislocating from the x_i (t) position to the x_i (t + 1) position. Modified hybrid metaheuristic of the search algorithm is proposed for constructing a classification model using recombination of swarm and genetic search algorithms. The first approach uses genetic algorithm initially and then the particle swarm algorithm. The second approach uses the high-level nesting hybridization method based on combination of genetic algorithm and particle swarm algorithm. The proposed approach to constructing a modified paradigm uses chromosomes with integer parameter values in the indicated hybrid algorithm and operators, which assist chromosomes to evolve according to the rules of particle swarm and genetic search

This work was performed with financial support provided by the Russian Foundation for Basic Research (grant no. 20-07-00260 a)

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