The paper solves the problem of generating informative patterns, which is the most important stage of logical data analysis. A greedy heuristic algorithm for pattern search is proposed, with the help of which a chain of patterns of different coverage and homogeneity is generated. Such a chain of patterns is an approximation of the Pareto optimal set of patterns, maximum in coverage of observations of the target class and minimum in coverage of observations of other classes. Computational experiments show that the choice of the pattern variant with the best informativeness (according to the used criterion of informativeness) avoids both the effect of retraining due to excessive selectivity of patterns and insufficient homogeneity of the pattern due to its excessive generalization.
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29 March 2024
PROCEEDINGS OF THE IV INTERNATIONAL CONFERENCE ON MODERNIZATION, INNOVATIONS, PROGRESS: Advanced Technologies in Material Science, Mechanical and Automation Engineering: MIP: Engineering-IV-2022
12–30 April 2022
Krasnoyarsk, Russian Federation
Research Article|
March 29 2024
Greedy algorithm for finding Pareto optimal patterns
Igor Masich;
Igor Masich
a)
Siberian Federal University
, Krasnoyarsk, Russia
a)Corresponding author: [email protected]
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Alena Stupina;
Alena Stupina
b)
Siberian Federal University
, Krasnoyarsk, Russia
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Katerina Ponomareva
Katerina Ponomareva
c)
Siberian Federal University
, Krasnoyarsk, Russia
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AIP Conf. Proc. 3021, 060036 (2024)
Citation
Igor Masich, Alena Stupina, Katerina Ponomareva; Greedy algorithm for finding Pareto optimal patterns. AIP Conf. Proc. 29 March 2024; 3021 (1): 060036. https://doi.org/10.1063/5.0194261
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