Adaptive Learning and Integrated Use of Information Flow Forecasting Methods

This research aims to improve quality indicators in solving classification and regression problems based on the adaptive selection of various machine learning models separate data samples from local segments. The proposed method combines different algorithms individual subsamples calculating qualitative selecting best sample Detecting changes time sequences makes it possible form where have properties (for example, variance, fraction, span, others). Data segmentation is used search for trend an algorithm points a series provide analytical information. experiment performance actual and, as result, obtained experimental values loss function classifiers segments entire sample. In terms practical novelty, use results increase problem solutions while developing methods. (F-measure, Accuracy, AUC) forecasting (RMSE) by 1%–8% average due assignment with Doi: 10.28991/ESJ-2023-07-03-03 Full Text: PDF