Abstract
This paper proposes a new framework for evaluation of set-based indices based on incremental sampling. Since these indices are defined by the relations between conditional attributes (R) and decision attribute(D), incremental sampling gives four possible cases according to the increment of sets for R or D. Using this classification, the behavior of indices can be evaluated for four cases. We applied this technique to several set-based indices. The results show that the evaluation framework gives a powerful tool for evaluation of set-based indices. Especially, it is found that the behavior of indices can be determined by a firstly given dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Breiman, L., Freidman, J., Olshen, R., Stone, C.: Classification And Regression Trees. Wadsworth International Group, Belmont (1984)
Cestnik, B., Kononenko, I., Bratko, I.: Assistant 86: A knowledge-elicitation tool for sophisticated users. In: EWSL, pp. 31–45 (1987)
Quinlan, J.: C4.5 - Programs for Machine Learning. Morgan Kaufmann, Palo Alto (1993)
Clark, P., Niblett, T.: The cn2 induction algorithm. Machine Learning 3 (1989)
Michalski, R., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system aq15 and its testing application to three medical domains. In: Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 1041–1045. AAAI Press, Menlo Park (1986)
Shan, N., Ziarko, W.: Data-based acqusition and incremental modification of classification rules. Computational Intelligence 11, 357–370 (1995)
Utgoff, P.E.: Incremental induction of decision trees. Machine Learning 4, 161–186 (1989)
Tsumoto, S., Hirano, S.: Incremental rules induction based on rule layers. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS, vol. 7414, pp. 139–148. Springer, Heidelberg (2012)
Tsumoto, S.: Automated induction of medical expert system rules from clinical databases based on rough set theory. Information Sciences 112, 67–84 (1998)
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers, Dordrecht (1991)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)
Greco, S., Słowiński, R., Szczęch, I.: Analysis of symmetry properties for bayesian confirmation measures. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS, vol. 7414, pp. 207–214. Springer, Heidelberg (2012)
Greco, S., Pawlak, Z., Slowinski, R.: Can bayesian confirmation measures be useful for rough set decision rules? Engineering Applications of Artificial Intelligence 17, 345–361 (2004)
Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.): RSKT 2012. LNCS, vol. 7414. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsumoto, S., Hirano, S. (2013). Evaluation of Incremental Change of Set-Based Indices. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-41299-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41298-1
Online ISBN: 978-3-642-41299-8
eBook Packages: Computer ScienceComputer Science (R0)