Multiclass Classifier Based on Binary Logistic Regressions Obtained According to the Principles of GMDH

Introduction. The issue of accuracy improvement in classification tasks is always topical, and various approaches have been developed, applied in accordance with the peculiarities of the problem formulation and properties of the feature space. Among the most effective models, classifiers based on multiple logistic regressions have proved themselves. Purpose. The aim of the paper is to develop an algorithm for solving multiclassification problems on the basis of binary logistic models built by the stepwise multiple logistic regression algorithm of the Stepwise type, improved according to the principles of the method of group accounting of arguments. Methods. The paper proposes a modification of the stepwise algorithm for creating binary multivariate logistic regressions Stepwise, where it is proposed to optimize the algorithm parameters in accordance with the principles of the method of group consideration of arguments: significance levels by the logarithmic likelihood ratio test for inclusion and exclusion of model arguments. The choice of optimal parameters is realized in accordance with an external criterion that takes into account the balance of classification accuracy of training and test samples and the balance of class classification accuracy. Subsequently, the binary class models obtained by the one-versus-all principle are combined into a multiclass classifier that returns the answer according to the maximum likelihood of the class. The comparison of classification models obtained by the classical Stepwise algorithm and the one proposed in the robot is carried out on the medical data of the publicly available Internet resource Kaggle. Conclusion. The paper substantiates and demonstrates the advantages of classifiers based on logistic multivariate regressions optimized according to the principles of the method of group consideration of arguments relative to the classical version of the Stepwise algorithm. The effective application of the algorithm in solving multiclass classification problem is shown.