Cost-Sensitive Pattern-Based classification for Class Imbalance problems

doi:10.1109/ACCESS.2019.2913982

Cost-Sensitive Pattern-Based classification for Class Imbalance problems

2019-01-01

21

OA

AI

O

Octavio Loyola‐González *

J

José Fco. Martínez-Trinidad

J

Jesús Ariel Carrasco-Ochoa

M

Milton García-Borroto

DOI：10.1109/ACCESS.2019.2913982

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In several problems, contrast pattern-based classifiers produce high accuracy and provide an explanation of the result in terms of the patterns used for classification. However, class imbalance problems are a great challenge for these classifiers because there exist significantly fewer objects belonging to a class regarding the remaining classes and this biases the classification to the majority class. Therefore, in this paper, we propose an algorithm for discovering cost-sensitive patterns in class imbalance problems and a pattern-based classifier which uses these patterns for classification. Our proposal follows the idea of fusing pattern discovery with the cost-sensitive approach for class imbalance problems. Our experiments show that our proposal obtains cost-sensitive patterns, which allow attaining significantly lower misclassification cost than using patterns mined by other well-known state-of-the-art pattern miners. Also, we show that our proposed pattern-based classifier is suitable for working with cost-sensitive patterns.

Keyword:

Pattern-based classification

imbalanced databases

cost-sensitive problems

Cost-Sensitive Pattern-Based classification for Class Imbalance problems

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