Methods of Lazy Classification Based on Interval Tuples

Student: Andrey Divavin

Educational Programme: Data Science (Master)

Final Grade: 9

Year of Graduation: 2024

This paper discusses a method for solving a classification problem on data with numerical features. In the course of the work, support-based aggregation functions that take into account the distribution of the data were proposed for the lazy classification method with FCA. Also, a randomization scheme for intersection search algorithms was proposed and their robustness was evaluated. In addition, the methods were compared with classical machine learning algorithms in terms of quality and interpretability. As a result of the study, it was shown that the proposed decision functions together with the randomization strategy can achieve comparable results with classical methods while remaining interpretable. Also, it was shown that randomized methods show reliable results in terms of variation of quality metrics.

Full text (added May 23, 2024)

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