Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study

Manuel S. Lazo-Cortés, José Fco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

© 2018, Springer International Publishing AG, part of Springer Nature. In Rough Set Theory, reducts are minimal subsets of attributes that retain the ability of the whole set of attributes to discern objects belonging to different classes. On the other hand, class-specific reducts allow discerning objects belonging to a specific class from all other classes. This latest type of reduct has been little studied. Here we show, through a case study, some advantages of using class-specific reducts instead of classic ones in a rule-based classifier. Our results show that it is worthwhile to deepen in the study of this issue.
Original languageAmerican English
Title of host publicationClass-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study
Pages23-30
Number of pages19
ISBN (Electronic)9783319921976
DOIs
StatePublished - 1 Jan 2018
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10880 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

Fingerprint

Reduct
Rough set theory
Set theory
Classifiers
Classifier
Attribute
Rough Set Theory
Class
Subset

Cite this

Lazo-Cortés, M. S., Martínez-Trinidad, J. F., & Carrasco-Ochoa, J. A. (2018). Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. In Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study (pp. 23-30). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10880 LNCS). https://doi.org/10.1007/978-3-319-92198-3_3
Lazo-Cortés, Manuel S. ; Martínez-Trinidad, José Fco ; Carrasco-Ochoa, Jesús Ariel. / Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. 2018. pp. 23-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Lazo-Cortés, MS, Martínez-Trinidad, JF & Carrasco-Ochoa, JA 2018, Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. in Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10880 LNCS, pp. 23-30, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/01/19. https://doi.org/10.1007/978-3-319-92198-3_3

Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. / Lazo-Cortés, Manuel S.; Martínez-Trinidad, José Fco; Carrasco-Ochoa, Jesús Ariel.

Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. 2018. p. 23-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10880 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lazo-Cortés MS, Martínez-Trinidad JF, Carrasco-Ochoa JA. Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. In Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study. 2018. p. 23-30. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-92198-3_3