22 June 2004 Quantization and similarity measure selection for discrimination of lymphoma subtypes under k-nearest neighbor classification
Author Affiliations +
Abstract
Molecular classification of tumors holds great potential for cancer research, diagnosis, and treatment. In this study, we apply a novel classification technique to cDNA microarray data for discriminating between three subtypes of malignant lymphoma: CD5+ diffuse large B-cell lymphoma, CD5- diffuse large B-cell lymphoma, and mantle cell lymphoma. The proposed technique combines the k-Nearest Neighbor (k-NN) algorithm with optimized data quantization. The feature genes on which the classification is based are selected by ranking them according to their separability criteria computed by taking into account between-class and within-class scatter. The classification errors, estimated using cross-validation, are significantly lower than those produced by classical variants of the k-NN algorithm. Multidimensional scaling and hierarchical clustering dendrograms are used to visualize the separation of the three subtypes of lymphoma.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cristian Mircean, Cristian Mircean, Ioan Tabus, Ioan Tabus, Jaakko Astola, Jaakko Astola, Tohru Kobayashi, Tohru Kobayashi, Hiroshi Shiku, Hiroshi Shiku, Motoko Yamaguchi, Motoko Yamaguchi, Ilya Shmulevich, Ilya Shmulevich, Wei Zhang, Wei Zhang, } "Quantization and similarity measure selection for discrimination of lymphoma subtypes under k-nearest neighbor classification", Proc. SPIE 5328, Microarrays and Combinatorial Techniques: Design, Fabrication, and Analysis II, (22 June 2004); doi: 10.1117/12.529580; https://doi.org/10.1117/12.529580
PROCEEDINGS
12 PAGES


SHARE
Back to Top