The Sequential Reduction Algorithm for Nearest Neighbor Rule Based on Double Sorting
An effective and strong reduction of large training sets is very important for the Nearest Neighbour Rule usefulness. In this paper, the reduction algorithm based on double sorting of a reference set is presented. The samples are sorted with the use of the representative measure and Mutual Neighbourhood Value proposed by Gowda and Krishna. Then, the reduced set is built by sequential adding and removing samples according to double sort order. The results of proposed algorithm are compared with results of well-known reduction procedures on nine real and one artificial datasets.
KeywordsHill Climbing Representative Measure Neighbor Rule Pattern Recognition Letter Pima Indian Diabetes
Unable to display preview. Download preview PDF.
- 1.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
- 9.Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. 14th Int. Joint Conf. Artificial Intelligence, pp. 338–345 (1995)Google Scholar
- 15.Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: 11th International Conference on Machine Learning, New Brunswick, NJ, USA, pp. 293–301 (1994)Google Scholar
- 16.The ELENA Project Real Databases, http://www.dice.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/
- 17.Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, Elsevier (2006)Google Scholar