Researh of Multi-label Data Classification Solutions

Abstract

The article analyzes the impact of the chosen method of model application on the classification of multi-label texts written in the Lithuanian language. The article presents a study of mult-label data classification methods in Lithuanian, which includes an analysis of the accuracy of the application of data classification methods for the automatic classification of multiclass texts written in Lithuanian. The classification methods, evaluation criteria, their applicability and the principles of data preparation for classification are reviewed. After preparing the text data for classification tasks, 44 combinations of classifiers were formed for the study and classification was performed using 3 different methods of multi-label data classification: category detection, category membership and category combination detection. The results obtained are compared in terms of time and classification accuracy, identifying the best performing classifiers and identifying the differences and advantages of the classification methods used.