A classifier of the Random Forest type based on GMDH, logistic transformation and positional voting
Yaroslav Hladkyi, Oleg Radchenko, Vladimir A. Pavlov, Олександр Матвійчук, Olena Horodetska · 2023
This study introduces an optimization of the voting function’s structure and parameters within a self-organizing forest of decision trees, based on an enhanced stepwise logistic regression algorithm and a positional voting method. The stepwise regression algorithm is improved through GMDH-based optimization for selecting threshold values for significance levels, both for the inclusion and exclusion of voting function arguments. The voting function’s arguments are formed using the positional method from branches of self-organizing trees and are supplemented by the forest’s input arguments. Classification results obtained from the standard version of Random Forest, the self-organizing forest, and the proposed enhanced version of the decision tree forest are compared.