回帰判別分析の改良

山崎 広之; 大軽 貴典; 岡本 晃典; 川下 理日人; 高原 淳一; 錦織 理華; 川瀬 雅也; 安永 照雄; 高木 達也

doi:10.11545/ciqs.2007.0.JP19.0

30th Symposium on Chemical Information and Computer Sciences, Kyoto

DOI https://doi.org/10.11545/ciqs.2007.0.JP19.0

Conference information

Host: Division of Chemical Information and Computer Science, The Chemical Society of Japan

Co-host: The Pharmaceutical Society of Japan, Japan Society for Bioscience, Biotechnology, and Agrochemistry, The Japan Society for Analytical Chemistry, Society of Computer Chemistry, Japan, Japanese Society for Information and Systems in Education (Approaval)

Poster Session

Refinement of Regression Discrimination Analysis

*Hiroyuki Yamasaki, Takanori Ohgaru, Kousuke Okamoto, Norihito Kawashita, Junichi Takahara, Rika Nishikiori, Masaya Kawase, Teruo Yasunaga, Tatsuya Takagi

Author information

*Hiroyuki Yamasaki
Graduate School of Phamaceutical Sciences. Osaka University
Takanori Ohgaru
Graduate School of Phamaceutical Sciences. Osaka University
Tanabe Seiyaku CO., LTD.
Kousuke Okamoto
Graduate School of Phamaceutical Sciences. Osaka University
Norihito Kawashita
Graduate School of Phamaceutical Sciences. Osaka University
Research Collaboration Center on Emerging and Re-emerging Infections, Osaka University
Junichi Takahara
Graduate School of Phamaceutical Sciences. Osaka University
Rika Nishikiori
Faculty of Pharmaceutical Sciences. Osaka Ohtani University
Masaya Kawase
Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University
Research Collaboration Center on Emerging and Re-emerging Infections, Osaka University
Faculty of Pharmaceutical Sciences. Osaka Ohtani University
Teruo Yasunaga
Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University
Research Collaboration Center on Emerging and Re-emerging Infections, Osaka University
Tatsuya Takagi
Graduate School of Phamaceutical Sciences. Osaka University
Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University
Research Collaboration Center on Emerging and Re-emerging Infections, Osaka University

Keywords: computational chemistry, regression analysis, discrimination analysis, quantification

CONFERENCE PROCEEDINGS FREE ACCESS

Pages JP19

Details

Abstract

A method called Regression Discrimination Analysis (RDA) has been developed for the discrimination of ordered categorical data. The method (RDA method) has the advantages of simultaneously considering any numbers of classes and faster computation than other methods. However, the RDA method is not utilized because the RDA method is inferior in recognition and prediction. The RDA method has two problems. First, although the RDA method needs parameters called class values, the class values are not specified. For this reason, the most adequate class values have to be determined for each time. Secondly, the borders of the RDA method are also not specified. The change of the borders has great influence on the result of discrimination. In this study, we suggest the optimized class values which are determined by searching for the normality in the data belonging each class value. We have refined the RDA method to some extent in this study.

Corresponding author

Register with J-STAGE for free!