Most lending institutions are aware of the importance of having a well-performing credit scoring model or scorecard and know that, in order to remain competitive in the credit industry, it is necessary to continuously improve their scorecards. This is because better scorecards result in substantial monetary savings that can be stated in terms of millions of dollars. Thus, there has been increasing interest in the application of new classifiers in credit scoring from both practitioners and researchers in the last few decades. Most of the recent work in this field has focused on the use of new and innovative techniques to classify applicants as either 'credit-worthy' or 'non-credit-worthy', with the aim of improving scorecard performance. In this thesis, we investigate the suitability of intelligent systems techniques for credit scoring. In particular, intelligent systems that use immunological metaphors are examined and used to build a learning and evolutionary classification algorithm. Our model, named Simple Artificial Immune System (SAIS), is based on the concepts of the natural immune system. The model uses applicants' credit details to classify them as either 'credit-worthy' or 'non-credit-worthy'. As part of the model development, we also investigate several techniques for selecting variables from the applicants' credit details. Variable selection is important as choosing the best set of variables can have a significant effect on the performance of scorecards. Interestingly, our results demonstrate that the traditional stepwise regression variable selection technique seems to perform better than many of the more recent techniques. A further contribution offered by this thesis is a detailed description of the scorecard development process. A detailed explanation of this process is not readily available in the literature and our description of the process is based on our own experiences and discussions with industry credit risk practitioners. We evaluate our model using both publicly available datasets as well as a very large set of real-world consumer credit scoring data obtained from a leading Australian bank. The evaluation results reveal that SAIS is a competitive classifier and is appropriate for developing scorecards which require a class decision as an outcome. Another conclusion reached is one confirmed by the existing literature, that even though more sophisticated scorecard development techniques, including SAIS, perform well compared to the traditional statistical methods, their performances are not statistically significantly different from the statistical methods. As with other intelligent systems techniques, SAIS is not explicitly designed to develop practical scorecards which require the generation of a score that represents the degree of confidence that an applicant will belong to a particular group. However, it is comparable to other intelligent systems techniques which are outperformed by statistical techniques for generating p ractical scorecards. Our final remark on this research is that even though SAIS does not seem to be quite suitable for developing practical scorecards, we still believe that there is room for improvement and that the natural immune system of the body has a number of avenues yet to be explored which could assist with the development of practical scorecards.

This thesis describes the development of a red tip/tilt and fringe detection system at the Sydney University Stellar Interferometer (SUSI), modelling the instrumental performance and effects of seeing at SUSI, making observations of Mira variable stars and finally modelling the atmospheres of Mira variables with physically self-consistent models. The new SUSI tip/tilt system is based around a CCD detector and has been successfully used to both track the majority of tip/tilt power in median seeing at an R magnitude of 4.5, and to provide seeing measures for post processing. The new fringe-detection system rapidly scans 33 to 140 $\mu$m in delay and detects the fringes using two avalanche-photodiodes. It has been used to acquire fringe data, provide user feedback and to track the fringe group-delay position. The system visibility (fringe visibility for a point source) and throughput were found to be consistent with models of the SUSI optical beam train. Observations were made of a variety of sources, including the Mira variables R Car and RR Sco, which were observed in two orthogonal polarization states. These measurements were the first successful use of Optical Interferometric Polarimetry (OIP), and enabled scattered light to be separated from bright photospheric flux. Dust scattering was found to originate from a thin shell 2-3 continuum radii from these stars, with an optical depth of 0.1 to 0.2 at 900 nm. Physical models of Mira variables including dust formation were developed, providing consistent explanations for these results as well as many other photometric and interferometric observations.

Mannheim, Univ., Diss., 2007.

Thesis (M.S.)--West Virginia University, 1998. / Title from document title page. "July 29, 1998." Document formatted into pages; contains xii, 100 p. : ill. (some col.) Vita. Includes abstract. Includes bibliographical references (p. 84-87).

Thesis (M. Phil.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 250-254).

Thesis (M. Phil.)--University of Hong Kong, 1995. / Includes bibliographical references (leaves 188-196).

Thesis (M.S.)--West Virginia University, 1999. / Title from document title page. Document formatted into pages; contains viii, 69 p. : ill. Includes abstract. Includes bibliographical references (p. 68-69).

Thesis (doctoral)--Kath. Universiẗat, Eichstätt, 2003.

Techn. University, Diss., 2004--Berlin.

This paper bridges the gap between variable selection methods (e.g., Pearson coefficients, KS test) and dimensionality reductionalgorithms (e.g., PCA, LDA). Variable selection algorithms encounter difficulties dealing with highly correlated data,since many features are similar in quality. Dimensionality reduction algorithms tend to combine all variables and cannotselect a subset of significant variables.Our approach combines both methodologies by applying variable selection followed by dimensionality reduction. Thiscombination makes sense only when using the same utility function in both stages, which we do. The resulting algorithmbenefits from complex features as variable selection algorithms do, and at the same time enjoys the benefits of dimensionalityreduction.1