Application of Multilevel Models in Classification and Regression Problems

There is a constant need to create methods for improving the quality indicators of information processing. In most practical cases, the ranges of target variables and predictors are formed under the influence of external and internal factors. Phenomena such as concept drift cause the model to lose its completeness and accuracy over time. The purpose of the work is to improve the processing data samples quality based on multi-level models for classification and regression problems. A two-level data processing architecture is proposed. At the lower level, the analysis of incoming information flows and sequences takes place, and the classification or regression tasks are solved. At the upper level, the samples are divided into segments, the current data properties in the subsamples are determined, and the most suitable lower-level models are assigned according to the achieved qualitative indicators. A formal description of the two-level architecture is given. In order to improve the quality indicators for classification and regression solving problems, a data sample preliminary processing is carried out, the model’s qualitative indicators are calculated, and classifiers with the best results are determined. The proposed solution makes it possible to implement constantly learning data processing systems. It is aimed at reducing the time spent on retraining models in case of data properties transformation. Experimental studies were carried out on several datasets. Numerical experiments have shown that the proposed solution makes it possible to improve the quality processing indicators. The model can be considered as an improvement of ensemble methods for processing information flows. Training a single classifier, rather than a group of complex classification models, makes it possible to reduce computational costs.

machine learning, multilevel models, purpose of classifying algorithms