<p> In this paper some fundamental concepts of information theory and their potential for classification construction in geography are discussed. The concepts of information and uncertainty are shown to be equivalent. Three different information measures are discussed and the particular situations for which each is appropriate are identified. With this background the available literature on the application of information theory to classification is reviewed and reinterpreted. A classification algorithm for objects characterized by multistate ordinal attributes is presented and tested. Recommendations for further research include the consideration of explicitly spatial information measures and the examination of more general information metrics.</p> / Thesis / Master of Arts (MA)

Indiana University-Purdue University Indianapolis (IUPUI) / Indices to assess malocclusion have been developed for either rating the 'severity of malocclusion' or for prioritizing a patient's need for orthodontic treatment. Most of these indices evaluate the malocclusion through examination of one or two of the following diagnostic records: dental casts, photographs, or clinical exam. However, no prior research had attempted to incorporate all of the above pretreatment orthodontic diagnostic records routinely taken for treatment evaluation. Pretreatment records (dental casts, intra-oral photographs, extra-oral photographs, panoramic radiograph, and a cephalogram, of sufficient quality to be reliably assessed) of 50 completed subjects treated at IUSD Graduate Orthodontic Clinic were evaluated. Subject criteria were a complete set of pretreatment orthodontic records, white non-Hispanic descent in the permanent dentition, absence of any craniofacial anomalies and known pathology. The 3M Unitek TM cephalometric protractor and electronic digital calipers accurate to the nearest tenth of a millimeter were used for measurements. Thirty-six measurements (11 cephalometric, 4 panoramic, 13 dental cast, and 8 photographic) were scored and combined into a total score that represents the new index. The peer assessment rating index (PAR Index) and a subjective ranking of 3 experienced orthodontists (each having more than 20 years of clinical orthodontic experience) were completed on all subjects for determining validity of the new index. All scoring methods were repeated on a subset of 10 random subjects to determine reliability. Statistical analysis showed significant correlations for the ability of the new index to detect severity of malocclusion. Also, the study showed a higher correlation for the new index in representing the experts' rankings than did the PAR index. It is the conclusion of this investigation that the new index is a valid index of malocclusion that more closely reflects experienced orthodontists rankings than the PAR index.

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Developments in sensor technology have made high resolution hyperspectral remote sensing data available to the remote sensing analyst for ground cover classification and target recognition tasks. Further, with limited ground-truth data in many real-life operating scenarios, such hyperspectral classification systems often employ dimensionality reduction algorithms. In this thesis, the efficacy of spectral derivative features for hyperspectral analysis is studied. These studies are conducted within the context of both single and multiple classifier systems. Finally, a modification of existing classification techniques is proposed and tested on spectral reflectance and derivative features that adapts the classification systems to the characteristics of the dataset under consideration. Experimental results are reported with handheld, airborne and spaceborne hyperspectral data. Efficacy of the proposed approaches (using spectral derivatives and single or multiple classifiers) as quantified by the overall classification accuracy (expressed in percentage), is significantly greater than that of these systems when exploiting only reflectance information.

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Country risk is an important concern in international business. Country risk classification refers to determining the risk level at which a country will not repay its international debt. Traditionally, country risk classification resorts to statistics methods such as discriminant analysis. In the past two decades, the so-called multicriteria decision aid (MCDA) methods have been proved to enjoy better performance than the standard statistics methods. Nevertheless, the performance of the MCDA methods is still far away from satisfactory and can be improved significantly. The better performance of several MCDA methods, such as UTADIS (UTilités Additives DIScriminantes) and MHDIS (Multigroup Hierarchical Discrimination), is achieved by exploiting the rater’s background knowledge. In the standard MCDA model, we assume that the criterion function for every factor is monotone and all the factors are independent. Then, we approximate the impact of every factor and use the sum of the corresponding criterion functions to determine the risk level of a country. By discretizing the feasible domain of the factor, the MCDA method solves a linear program to find a classifier for country risk classification. This thesis tries to enhance the capability of MCDA methods by allowing a class of non-monotone criteria: the unimodal ones. For this purpose, we developed an integer quadratic (non-convex) program for general unimodal criteria. Further, if we restrict ourselves to convex or concave unimodal criteria, then we can still use a linear program to find a classifier. For the case where all the factors are correlated, a simple quadratic form of aggregation is proposed to deal with it. Compared with the original UTADIS model, our generalized model is more flexible and can deal with more complex scenarios. Finally, our generalized model is tested based on cross-validation and our experiment is carried out under the AMPL+sovers environment. Promising numeric results indicate that except for its theoretical advantages, our generalized model exhibits practical efficiency and robustness as well. / Thesis / Master of Science (MS)