Bagging-based instance selection for instance-based classification
Dmytro Kavrin, С. А. Субботин · 2020
The task of reducing marked large size samples for building diagnostic and recognizing models by precedents is considered.The method allowing to reduce essentially the size of a training sample increasing at the same time its efficiency, through removal of irrelevant and redundant instances is proposed.The given method provides an opportunity to estimate each instance of a training sample by synthesis of an ensemble of weak classifiers using a bagging model, and to create a reduced sample of the most significant instances by estimations.Software is developed to implement the proposed method.This software has been experimentally investigated in solving the task of reducing synthetic and real world data.The results of the conducted experiments allow recommending the use of the developed method and its software realization for solving the task in the sphere of technical diagnostics.