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All Journal Emerging Science Journal
Ilya S. Lebedev
St. Petersburg Federal Research Centre of the Russian Academy of Sciences, 39, 14th Line V.O., St. Petersburg, 199178,
Environmental Science

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Journal : Emerging Science Journal

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Adaptive Learning and Integrated Use of Information Flow Forecasting Methods Ilya S. Lebedev; Mikhail E. Sukhoparov
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-03

Abstract

This research aims to improve quality indicators in solving classification and regression problems based on the adaptive selection of various machine learning models on separate data samples from local segments. The proposed method combines different models and machine learning algorithms on individual subsamples in regression and classification problems based on calculating qualitative indicators and selecting the best models on local sample segments. Detecting data changes and time sequences makes it possible to form samples where the data have different properties (for example, variance, sample fraction, data span, and others). Data segmentation is used to search for trend changes in an algorithm for points in a time series and to provide analytical information. The experiment performance used actual data samples and, as a result, obtained experimental values of the loss function for various classifiers on individual segments and the entire sample. In terms of practical novelty, it is possible to use the obtained results to increase quality indicators in classification and regression problem solutions while developing models and machine learning methods. The proposed method makes it possible to increase classification quality indicators (F-measure, Accuracy, AUC) and forecasting (RMSE) by 1%–8% on average due to segmentation and the assignment of models with the best performance in individual segments. Doi: 10.28991/ESJ-2023-07-03-03 Full Text: PDF
Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering Ilya S. Lebedev; Mikhail E. Sukhoparov
Emerging Science Journal Vol 8, No 1 (2024): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-01-025

Abstract

This paper presents a solution for building and implementing data processing models and experimentally evaluates new possibilities for improving ensemble methods based on multilevel data processing models. This study proposes a model to reduce the cost of retraining models when transforming data properties. The research objective is to improve the quality indicators of machine learning models when solving classification problems. The novelty is a method that uses a multilevel architecture of data processing models to determine the current data properties in segments at different levels and assign algorithms with the best quality indicators. This method differs from the known ones by using several model levels that analyze data properties and assign the best models to individual segments of data and training. The improvement consists of using unsupervised clustering of data samples. The resulting clusters are separate subsamples for assigning the best machine-learning models and algorithms. Experimental values of quality indicators for different classifiers on the whole sample and different segments were obtained. The findings show that unsupervised clustering using multilevel models can significantly improve the quality indicators of “weak” classifiers. The quality indicators of individual classifiers improve when the number of data clusters is increased to a certain threshold. The results obtained are applicable to classification when developing models and machine learning methods. The proposed method improved the classification quality indicators by 2–9% due to segmentation and the assignment of models with the best quality indicators in individual segments. Doi: 10.28991/ESJ-2024-08-01-025 Full Text: PDF
Co-Authors Mikhail E. Sukhoparov