Abstract
Classification problem asks to construct a classifier from a given data set, where a classifier is required to capture the hidden oracle of the data space. Recently, we introduced a new class of classifiers ICF, which is based on iteratively composed features on {0,1, ∗ }-valued data sets. We proposed an algorithm ALG-ICF ∗ to construct an ICF classifier and showed its high performance. In this paper, we extend ICF so that it can also process real world data sets consisting of numerical and/or categorical attributes. For this purpose, we incorporate a discretization scheme into ALG-ICF ∗ as its preprocessor, by which an input real world data set is transformed into {0,1, ∗ }-valued one. Based on the experimental studies on conventional discretization schemes, we propose a new discretization scheme, integrated construction (IC). Our computational experiments reveal that the ALG-ICF ∗ equipped with IC outperforms a decision tree constructor C4.5 in many cases.
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
Preview
Unable to display preview. Download preview PDF.
References
Fayyad, U., Piatetsky-Shapiro, G., Padhraic, S.: From data mining to knowledge discovery in databases. AI Magazine 17(3), 37–54 (1996)
Weiss, S.M., Kulikowski, C.A.: Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann, San Francisco (1991)
Haraguchi, K., Ibaraki, T.: Construction of classifiers by iterative compositions of features with partial knowledge. IEICE Trans. Fund. Elec. Comm. and Comp. Sci. E89-A(5), 1284–1291 (2006)
Bohanec, M., Zupan, B.: A function-decomposition method for development of hierarchical multi-attribute decision models. Dec. Supp. Sys. 36(3), 215–233 (2004)
Boros, E., Gurvich, V., Hammer, P.L., Ibaraki, T., Kogan, A.: Decomposability of partially defined boolean function. Disc. Appl. Math. 62, 51–75 (1995)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Chow, C.K.: On optimum recognition error and reject tradeoff. IEEE Trans. Inf. Th. 16(1), 41–46 (1970)
Domingos, P., Pazzani, M.J.: Beyond independence: Conditions for the optimality of the simple bayesian classifier. In: Saitta, L. (ed.) Proc. 13th Int’l Conf. Mach. Learn. pp. 105–112 (1996)
Elomaa, T., Rousu, J.: Fast minimum training error discretization. In: Sammut, C., Hoffmann, A. (eds.) Proc. 19th Int’l Conf. Mach. Learn, pp. 131–138 (2002)
Mii, S.: Feature determination algorithms in the analysis of data. Master’s thesis, Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University (2001)
Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Haraguchi, K., Nagamochi, H. (2007). Extension of ICF Classifiers to Real World Data Sets. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_77
Download citation
DOI: https://doi.org/10.1007/978-3-540-73325-6_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
eBook Packages: Computer ScienceComputer Science (R0)