Binary Decision Tree Construction using the Hybrid Swarm Intelligence

Abstract

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 Mk route from the root to the dangling vertex. Decision tree developed interpretation is presented as a pair of chromosomes (Sk, Wk). The Sk chromosome list of genes corresponds to the list of all attributes included in the Mk route in the decision tree. The Wk chromosome gene values correspond to the attribute values included in the Mk 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 αi particle to three xi (t), x*i (t), x*(t) attractors dislocating from the xi (t) position to the xi (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