Abstract
The paper proposes an oddness measure for estimating the extent to which a new item is at odds with a class. Then a simple classification procedure based on the minimization of oddness with respect to the different classes is proposed. Experiments on standard benchmarks with Boolean or numerical data provide good results. The global oddness measure is based on the estimation of the oddness of the new item with respect to the subsets of the classes having a given size.
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.
Similar content being viewed by others
References
Benedetto, D., Caglioti, E., Loreto, V.: Language trees and zipping. Phys. Review Lett. 88(4) (2002)
Bounhas, M., Prade, H., Richard, G.: Analogical classification: handling numerical data. In: Straccia, U., Calì, A. (eds.) SUM 2014. LNCS, vol. 8720, pp. 66–79. Springer, Heidelberg (2014)
Bounhas, M., Prade, H., Richard, G.: A new view of conformity and its application to classification. In: Destercke, S., Denoeux, T. (eds.) ECSQARU 2015. LNAI, vol. 9161. Springer, Heidelberg (2015)
Bounhas, M., Prade, H., Richard, G.: Analogical classification: a new way to deal with examples. In: ECAI 2014–21st European Conference on Artificial Intelligence, 18–22 August 2014, Prague, Czech Republic. Frontiers in Artificial Intelligence and Applications, vol. 263, pp. 135–140. IOS Press (2014)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Mertz, J., Murphy, P.: Uci repository of machine learning databases (2000). ftp://ftp.ics.uci.edu/pub/machine-learning-databases
Miclet, L., Bayoudh, S., Delhay, A.: Analogical dissimilarity: definition, algorithms and two experiments in machine learning. JAIR 32, 793–824 (2008)
Prade, H., Richard, G.: From analogical proportion to logical proportions. Logica Universalis 7(4), 441–505 (2013)
Prade, H., Richard, G.: Homogenous and heterogeneous logical proportions. IfCoLog J. of Logics and their Applications 1(1), 1–51 (2014)
Sculley, D., Brodley, C.E.: Compression and machine learning: a new perspective on feature space vectors. In: Proc. of the Data Compressing Conference DCC, pp. 332–341. IEEE (2006)
Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. Int. Conf. on Machine Learning, pp. 444–453 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bounhas, M., Prade, H., Richard, G. (2015). Oddness-Based Classifiers for Boolean or Numerical Data. In: Hölldobler, S., , Peñaloza, R., Rudolph, S. (eds) KI 2015: Advances in Artificial Intelligence. KI 2015. Lecture Notes in Computer Science(), vol 9324. Springer, Cham. https://doi.org/10.1007/978-3-319-24489-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-24489-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24488-4
Online ISBN: 978-3-319-24489-1
eBook Packages: Computer ScienceComputer Science (R0)