A method of successive elimination of spurious arguments for effective solution the search-based modelling tasks. Oleksandr Samoilenko, Volodymyr Stepashko

Abstract. Previously we considered GMDH algorithms for solving the problems with a large number of arguments based on algorithms with successive selection of the most informative arguments. These algorithms build models very quickly but the accuracy of these models is not always sufficient. The quality of models built in such a way depends on the quality of arguments selection. Thus improvement of the method with successive selection of arguments for the rising of the quality and effectiveness of the informative arguments selection is the main goal of this paper.

To rise the quality of arguments extraction in the algorithm with successive selection the inverted structures are used. As a result we get very high quality increase but the speed of the models building is considerably reduced. Some additional modifications of this algorithm are solved this problem.

This paper considers the main aspects of the algorithm and results of its performing. The experiments with the algorithm of successive elimination of spurious arguments using inverse structures were carried out. The results of the realized experiments confirm effectiveness of this method. Use of the algorithm enables to essentially accelerate the retrieval for the best subset of regressors and to solve tasks with considerably larger number of regressors compared with ordinary combinatorial GMDH algorithm of exhaustive search of arguments.

Keywords. Inductive modelling, GMDH, combinatorial algorithm, argument, successive selection, successive elimination, spurious arguments.

References.
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