GA-facilitated classifier optimization with varying similarity measures

ABSTRACT

Genetic algorithms are powerful tools for k-nearest neighbors classification. Traditional knn classifiers employ Euclidian distance to assess neighbor similarity, though other measures may also be used. GAs can search for optimal linear weights of features to improve knn performance using both Euclidian distance and cosine similarity. GAs also optimize additive feature offsets in search of an optimal point of reference for assessing angular similarity using the cosine measure. This poster explores weight and offset optimization for knn with varying similarity measures, including Euclidian distance (weights only), cosine similarity, and Pearson correlation. The use of offset optimization here represents a novel technique for enhancing Pearson/knn classification performance. Experiments compare optimized and non-optimized classifiers using public domain datasets. While unoptimized Euclidian knn often outperforms its cosine and Pearson counterparts, optimized Pearson and cosine knn classifiers show equal or improved accuracy compared to weight-optimized Euclidian knn.

References

1. M. P. S. Brown, W. N. Grundy, D. Lin, N. Cristianini, C. W. Sugnet, T. S. Furey, M. A. Jr., and D. Haussler. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Science, 97:262--267, 2000.]]
2. E. Han, G. Karypis, and V. Kumar. Text categorization using weight adjusted k-nearest neighbor classification. In Advances in Knowledge Discovery and Data Mining: fifth Pacific-Asia Conference, pages 53--65, 2001.]]
3. M. R. Peterson, T. E. Doom, and M. L. Raymer. Ga-facilitated knowledge discovery and pattern recognition optimization applied to the biochemistry of protein solvation. In GECCO 2004 Proceedings, LNCS 3102, pages 426--437, 2004.]]
4. M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, and A. K. Jain. Dimensionality reduction using genetic algorithms. IEEE Trans Evol. Comp., 4(5):164--171, 2000.]]
5. W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pat. Rec. Letters, 10:335--347, 1989.]]

Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning algorithms

      • Feature selection

    • Machine learning approaches

      • Classification and regression trees