Abstract
Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease appears more rarely than the normal case. In such a situation classifiers that generalize over the data such as decision trees and Naïve Bayesian are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the performance to decision trees and Naïve Bayesian. Finally, we give an outlook for further work.
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
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Chang, C.-L.: Finding Prototypes for Nearest Neighbor Classifiers. IEEE Trans. on Computers C-23(11), 1179–1184 (1974)
Perner, P.: Data Mining on Multimedia Data. LNCS, vol. 2558. Springer, Heidelberg (2002)
Wettschereck, D., Aha, D.W.: Weighting Features. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 347–358. Springer, Heidelberg (1995)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based Learning Algorithm. Machine Learning 6(1), 37–66 (1991)
Perner, P.: Prototype-Based Classification. Applied Intelligence 28, 238–246 (2008)
Perner, P., Zscherpel, U., Jacobsen, C.: A Comparision between Neural Networks and Decision Trees based on Data from Industrial Radiographic Testing. Pattern Recognition Letters 22, 47–54 (2001)
Smyth, B., McKenna, E.: Modelling the Competence of Case-Bases. In: Advances in Case-Based Reasoning, 4th European Workshop, Dublin, Ireland, pp. 208–220 (1998)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
DECISION MASTER, http://www.ibai-solutions.de
Horton, P.: Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier. In: Proceeding of the International Conference on Intelligent Systems in Molecular Biology, pp. 147–152 (1997)
Ratanamahatana, C.A., Gunopulos, D.: Scaling up the Naive Bayesian Classifier: Using Decision Trees for Feature Selection. In: Proceedings of Workshop on Data Cleaning and Preprocessing (DCAP 2002), at IEEE International Conference on Data Mining (ICDM 2002), Maebashi, Japan (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Little, S., Salvetti, O., Perner, P. (2008). Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications. In: Althoff, KD., Bergmann, R., Minor, M., Hanft, A. (eds) Advances in Case-Based Reasoning. ECCBR 2008. Lecture Notes in Computer Science(), vol 5239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85502-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-85502-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85501-9
Online ISBN: 978-3-540-85502-6
eBook Packages: Computer ScienceComputer Science (R0)