Reward-Punishment Editing for Mixed Data
The KNN rule has been widely used in many pattern recognition problems, but it is sensible to noisy data within the training set, therefore, several sample edition methods have been developed in order to solve this problem. A. Franco, D. Maltoni and L. Nanni proposed the Reward-Punishment Editing method in 2004 for editing numerical databases, but it has the problem that the selected prototypes could belong neither to the sample nor to the universe. In this work, we propose a modification based on selecting the prototypes from the training set. To do this selection, we propose the use of the Fuzzy C-means algorithm for mixed data and the KNN rule with similarity functions. Tests with different databases were made and the results were compared against the original Reward-Punishment Editing and the whole set (without any edition).
KeywordsWrong Classification Object Description Representative Object Pattern Recognition Problem Mixed Data
- 2.Paredes, R., Wagner, T.: Weighting prototypes, a new approach. In: The proceedings of International Conference on Pattern Recognition (ICPR), vol. II, pp. 25–28 (2000)Google Scholar
- 3.Franco, A., Maltoni, D., Nanni, L.: Reward- Punishment Editing. In: The proceedings of International Conference on Pattern Recognition, ICPR (2004) (In CD)Google Scholar
- 4.Ayaquica-Martínez, I.O., Martínez-Trinidad, J.F.: Fuzzy C-means algorithm to analyze mixed data. In: The proceedings of the 6th Iberoamerican Symposium on Pattern Recognition, Florianópolis, Brazil, pp. 27–33 (2001)Google Scholar
- 5.Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html