Comparison between two prototype representation schemes for a nearest neighbor classifier

The paper deals with the problem of finding good prototypes for a condensed nearest neighbor classified in a recognition system. A comparison study is done between two prototype representation schemes. The prototype search is done by a genetic algorithm which is able to generate novel prototypes (i.e. prototypes which are not among the training samples). It is shown that the generalized representation scheme is more powerful, giving significantly larger normalized interclass distances. It is also shown that both representation schemes with generic algorithm give significantly better prototypes than a direct prototype selection algorithm, which can select only among the training samples.

[1]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[2]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[3]  J. Kangas.  Prototype search for a nearest neighbor classifier by a genetic algorithm , 1999, Proceedings Third International Conference on Computational Intelligence and Multimedia Applications. ICCIMA'99 (Cat. No.PR00300).

[4]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[5]  Herbert Freeman,et al.  On the Encoding of Arbitrary Geometric Configurations , 1961, IRE Trans. Electron. Comput..

[6]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[7]  Hung Yuen.  A chain coding approach for real-time recognition of on-line handwritten characters , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[8]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[9]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .

[10]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[11]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[12]  Shane S. Sturrock,et al.  Time Warps, String Edits, and Macromolecules – The Theory and Practice of Sequence Comparison . David Sankoff and Joseph Kruskal. ISBN 1-57586-217-4. Price £13.95 (US$22·95). , 2000 .