Session ID: SA-G4-2 / Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems, September 17-21, 2008, Nagoya University, Nagoya, Japan

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of risk. The results shows that the logistic regression outperforms the discriminant analysis in classifying all of the groups except for the middle one. We conclude that this is in line with previous studies.

This thesis stems from my research into the broad area of (credit) scoring and the predicting, understanding and explaining of consumer behaviour. This research started at the Univers1ty of Edinburgh on an ESRC funded project in 1988. This work, which is being submitted as the partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough Unvers1ty, consists of an introductory chapter and a selection of papers published 1991 - 2001 (inclusive). The papers address some of the key issues and areas of interest and concern arising from the rapidly evolving and expanding credit (card) market and the highly competitive nature of the credit industry. These features were particularly evident during the late 1980's and throughout the 90's Chapter One provides a general background to the research and outlines some of the key (practical) issues involved in building a (credit) scorecard Additionally, it provides a brief summary of each of the research papers appearing in full in Chapters 2- 9 (inclusive) and ends with some general limitations and conclusions. The research papers appearing in Chapters 2-9 inclusive) are all concerned with predicting, understanding and explaining different types of consumer behaviour in relation to the use of credit cards. For example discriminating between 'GOOD' and 'BAD' repayers of credit card debt on the basis of different definitions of good and bad, the identification of 'slow payers' using different statistical methods; examining the characteristics of credit card users and non-users, and identifying the characteristics of credit card holders most likely to return their credit card.

The aim of this research was to assemble sufficient experimental evidence about candidate gene transcript expression changes between non-neoplastic and neo- plastic colorectal tissues to justify future assay development involving promis- ing leads. To achieve this aim, this thesis explores the hypothesis that gene expression-based biomarkers can be used to accurately discriminate colorectal neoplastic tissues from non-neoplastic controls. This hypothesis was tested by first analysing multiple, large, quality controlled data sets comprising gene expression measurements across colorectal phenotypes to discover potential biomarkers. Candidate biomarkers were then subjected to validation testing using a custom-design oligonucleotide microarray applied to independently derived clinical specimens. A number of novel conclusions are reached based on these data. The most important conclusion is that a defined subset of genes expressed in the colorectal mucosa are reliably differentially ex- pressed in neoplastic tissues. In particular, the apparently high prediction accu- racy achieved for single gene transcripts to discriminate hundreds of neoplastic and non-neoplastic tissues provides compelling evidence that the resulting can- didate genes are worthy of further biomarker research. In addition to addressing the central hypothesis, additional contributions are made to the field of colorectal neoplasia gene expression profiling. These contributions include: The first systematic analysis of gene expression in non-diseased tissues along the colorectum To better understand the range of gene expression in non-diseased tissues, RNA extracts taken from along the longitudinal axis of the large intestine were studied. The development of quality control methodologies for high dimen- sional gene expression data Complex data collection platforms such as oligonucleotide microarrays introduce the potential for unrecognized confound- ing variables. The exploration of quality control parameters across five hundred microarray experiments provided insights about quality control techniques. The design of a custom microrray comprised of oligonucleotide probe- sets hybridising to RNA transcripts differentially expressed in neo- plastic colorectal specimens A custom design oligonucleotide microarray was designed and tested combining the results of multiple biomarker discovery projects. Introduction of a method to filter differentially expressed genes dur- ing discovery that may improve validation efficiencies of biomarker discovery based on gene expression measurements Differential expression discovery research is typically focused only on quantitative changes in transcript concentration between phenotype contrasts. This work introduces a method for generating hypotheses related to transcripts which may be quali- tatively “switched-on” between phenotypes. Identification of mRNA transcripts which are differentially expressed between colorectal adenomas and colorectal cancer tissues Transcripts differentially expressed between adenomatous and cancerous RNA extracts were discovered and then tested in independent tissues. In conclusion, these results confirm the hypothesis that gene expression profiling can discriminate colorectal neoplasia (including adenomas) from non-neoplastic controls. These results also establish a foundation for an ongoing biomarker development program.

Thesis (M.Sc.)--York University, 2006. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 130-134). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR19748

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 101-107).

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (p. 159-169).

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references.

Thesis (M. Sc.), Memorial University of Newfoundland, 1998. / Bibliography: leaves 95-99.

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006. / Prausnitz, Mark, Committee Member ; Vidakovic, Brani, Committee Member ; Lee, Eva, Committee Chair ; Nemhauser, George, Committee Member ; Johnson, Ellis, Committee Member. Includes bibliographical references.