Abstract
We consider a rough set inspired approach to deriving meaningful attribute subsets from data organized in a form of a decision system. We focus on quality functions measuring degrees in which particular attribute subsets determine the values of a decision attribute. We follow a well known idea of assigning weights to the training objects in order to reflect their importance in the attribute subset selection and new case classification processes. We discuss an example of an object weighting strategy related to probabilities of decision classes in the training data. We show that two attribute subset quality functions used in our earlier research are the same function computed using two different weighting techniques. We also investigate whether it is worth using the same weights during the processes of attribute selection and new case classification.
This research was partly supported by Polish National Science Centre (NCN) grants DEC-2011/01/B/ST6/03867 and DEC-2012/05/B/ST6/03215.
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
Świniarski, R.W., Skowron, A.: Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters 24(6), 833–849 (2003)
Bazan, J., Szczuka, M.S.: The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005)
Ślęzak, D.: Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds.) Transactions on Computational Science V. LNCS, vol. 5540, pp. 182–205. Springer, Heidelberg (2009)
Widz, S., Ślęzak, D.: Rough Set Based Decision Support – Models Easy to Interpret. In: Peters, G., Lingras, P., Ślęzak, D., Yao, Y. (eds.) Rough Sets: Selected Methods and Applications in Management & Engineering. Advanced Information and Knowledge Processing, pp. 95–112. Springer (2012)
Kuncheva, L.I., Diez, J.J.R., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random Subspace Ensembles for fMRI Classification. IEEE Transactions on Medical Imaging 29(2), 531–542 (2010)
Ślęzak, D.: Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis. Fundamenta Informaticae 44(3), 291–319 (2000)
Stawicki, S., Widz, S.: Decision Bireducts and Approximate Decision Reducts: Comparison of Two Approaches to Attribute Subset Ensemble Construction. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems – FedCSIS 2012, Wrocław, Poland, September 9-12, pp. 331–338. IEEE (2012)
Pawlak, Z., Skowron, A.: Rudiments of Rough Sets. Information Sciences 177(1), 3–27 (2007)
Skurichina, M., Duin, R.P.W.: Bagging, Boosting and the Random Subspace Method for Linear Classifiers. Pattern Analysis and Applications 5(2), 121–135 (2002)
Ślęzak, D., Ziarko, W.: The Investigation of the Bayesian Rough Set Model. International Journal of Approximate Reasoning 40(1-2), 81–91 (2005)
Frank, A., Asuncion, A.: UCI Machine Learning Repository (2010), http://archive.ics.uci.edu/ml
Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The Balanced Accuracy and Its Posterior Distribution. In: 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, August 23-26, pp. 3121–3124. IEEE (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Widz, S., Ślęzak, D. (2014). Attribute Subset Quality Functions over a Universe of Weighted Objects. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-08729-0_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08728-3
Online ISBN: 978-3-319-08729-0
eBook Packages: Computer ScienceComputer Science (R0)