Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bache, K., Lichman, M.: UCI machine learning repository. School of Information and Computer Science, University of California, Irvine, CA (2013). http://archive.ics.uci.edu/ml
Baggenstoss, P.M.: Class-specific feature sets in classification. IEEE Trans. Signal Process. 47, 3428–3432 (1999)
Barman, T., Rajesh, G., Archana, R.: Rough set based segmentation and classification model for ECG. In: Conference on Advances in Signal Processing (CASP), pp. 18–23. IEEE (2016)
Cohen, W.W.: Fast effective rule induction. In: Machine Learning: Proceedings of the Twelfth International Conference, Lake Tahoe California, USA, pp. 1–10 (1995)
Cohen, W.W., Singer, Y.: A simple, fast, and effective rule learner. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence, Orlando Florida, USA, pp. 335–342 (1999)
Gao, C., Yao, Y.: An addition strategy for reduct construction. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds.) RSKT 2014. LNCS (LNAI), vol. 8818, pp. 535–546. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11740-9_49
Jia, X.Y., Shang, L., Zhou, B., Yao, Y.Y.: Generalized attribute reduct in rough set theory Knowl. Based Syst. 91, 204–218 (2016)
Liang, J.Y., Wang, F., Dang, C.Y., Qian, Y.H.: A group incremental approach to feature selection applying rough set technique. IEEE Trans. Knowl. Data Eng. 26, 294–308 (2014)
Miao, D.Q., Zhao, Y., Yao, Y.Y., Li, H.X., Xu, F.F.: Reducts in consistent and inconsistent decision tables of the Pawlak rough set model. Inf. Sci. 179(24), 4140–4150 (2009)
Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)
Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning About Data, pp. 315–330. Kluwer Academic Publishers, Dordrecht (1992)
Pineda-Bautista, B.B., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: General framework for class-specific feature selection. Expert Syst. Appl. 38, 10018–10024 (2011)
Quinlan, J.R.: MDL and categorical theories (continued). In: Machine Learning: Proceedings of the Twelfth International Conference, Lake Tahoe California, USA, pp. 464–470 (1995)
Rana, H., Lal, M.: A rough set theory approach for rule generation and validation using RSES. Int. J. Rough Sets Data Anal. 3(1), 55–70 (2016)
Rana, H., Lal, M.: A comparative study based on rough set and classification via clustering approaches to handle incomplete data to predict learning styles. Int. J. Decis. Supp. Syst. Technol. 9(2), 1–20 (2017)
Skowron, A., Bazan, J., Szczuka, M., Wroblewski, J.: Rough set exploration system (version 2.2.2). http://logic.mimuw.edu.pl/~rses/
Triguero, I., Gonzlez, S., Moyano, J.M., Garca, S., Alcal-Fdez, J., Luengo, J., Fernndez, A., del Jesus, M.J., Snchez, L., Herrera, F.: KEEL 3.0: an open source software for multi-stage analysis in data mining. Int. J. Comput. Intell. Syst. 10, 1238–1249 (2017)
Wang, G.Y., Ma, X.A., Yu, H.: Monotonic uncertainty measures for attribute reduction in probabilistic rough set model. Int. J. Approx. Reason. 59, 41–67 (2015)
Wang, L.P., Wang, Y.L., Chang, Q.: Feature selection methods for big data bioinformatics: a survey from the search perspective. Methods 111, 21–31 (2016)
Yao, Y., Fu, R.: The concept of reducts in Pawlak three-step rough set analysis. In: Peters, J.F., Skowron, A., Ramanna, S., Suraj, Z., Wang, X. (eds.) Transactions on Rough Sets XVI. LNCS, vol. 7736, pp. 53–72. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36505-8_4
Yao, Y.Y., Zhang, X.: Class-specific attribute reducts in rough set theory. Inf. Sci. 418(419), 601–618 (2017)
Zhang, X.Y., Miao, D.Q.: Double-quantitative fusion of accuracy and importance: systematic measure mining, benign integration construction, hierarchical attribute reduction. Knowl. Based Syst. 91, 219–240 (2016)
Zhang, M.L., Wu, L.: LIFT: multi-label learning with label-specific features. IEEE Trans. Pattern Anal. Mach. Intell. 37, 107–120 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-92198-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92197-6
Online ISBN: 978-3-319-92198-3
eBook Packages: Computer ScienceComputer Science (R0)
