Identifying Enlisted Stay and Leave Population Characteristics with Discriminant Analysis

The research contribution of this thesis is the first known integrated architecture and feature selection algorithm for Radial Basis Neural Networks (RBNNs). The objective is to apply the network iteratively to determine the final architecture and feature set used to evaluate a problem. Additionally, this thesis compares three different classification techniques, Discriminant Analysis (DA), Feed-Forward Neural Networks (FFN) and RBNNs against several hard to solve problems. These problems were used to evaluate general classifier performance as well as the performance of the feature selection techniques. This thesis describes the classification techniques as well as the measures used to evaluate them. It next develops a new clustering technique used to determine the network architecture and the saliency measure used to select features for RBNNs. Next, the thesis applies these techniques to three general problems, Block-C, the University of Wisconsin Breast Cancer Data (UWBCD) and a noise corrupted version of Fisher\u27s Iris problem. Finally, the conclusions and recommendations for future research are provided

A weighted regional voting based ensemble of multiple classifiers for face recognition.

Face recognition is heavily studied for its wide range of application in areas such as information security, law enforcement, surveillance of the environment, entertainment, smart cards, etc. Competing techniques have been proposed in computer vision conferences and journals, no algorithm has emerged as superior in all cases over the last decade. In this work, we developed a framework which can embed all available algorithms and achieve better results in all cases over the algorithms that we have embedded, without great sacrifice in time complexity. We build on the success of a recently raised concept - Regional Voting. The new system adds weights to different regions of the human face. Different methods of cooperation among algorithms are also proposed. Extensive experiments, carried out on benchmark face databases, show the proposed system's joint contribution from multiple algorithms is faster and more accurate than Regional Voting in every case. --P. ix.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b180553

Recherche d'images par le contenu, analyse multirésolution et modèles de régression logistique

Cette thèse, présente l'ensemble de nos contributions relatives à la recherche d'images par le contenu à l'aide de l'analyse multirésolution ainsi qu'à la classification linéaire et nonlinéaire. Dans la première partie, nous proposons une méthode simple et rapide de recherche d'images par le contenu. Pour représenter les images couleurs, nous introduisons de nouveaux descripteurs de caractéristiques qui sont des histogrammes pondérés par le gradient multispectral. Afin de mesurer le degré de similarité entre deux images d'une façon rapide et efficace, nous utilisons une pseudo-métrique pondérée qui utilise la décomposition en ondelettes et la compression des histogrammes extraits des images. Les poids de la pseudo-métrique sont ajustés à l'aide du modèle classique de régression logistique afin d'améliorer sa capacité à discriminer et la précision de la recherche. Dans la deuxième partie, nous proposons un nouveau modèle bayésien de régression logistique fondé sur une méthode variationnelle. Une comparaison de ce nouveau modèle au modèle classique de régression logistique est effectuée dans le cadre de la recherche d'images. Nous illustrons par la suite que le modèle bayésien permet par rapport au modèle classique une amélioration notoire de la capacité à discriminer de la pseudo-métrique et de la précision de recherche. Dans la troisième partie, nous détaillons la dérivation du nouveau modèle bayésien de régression logistique fondé sur une méthode variationnelle et nous comparons ce modèle au modèle classique de régression logistique ainsi qu'à d'autres classificateurs linéaires présents dans la littérature. Nous comparons par la suite, notre méthode de recherche, utilisant le modèle bayésien de régression logistique, à d'autres méthodes de recherches déjà publiées. Dans la quatrième partie, nous introduisons la sélection des caractéristiques pour améliorer notre méthode de recherche utilisant le modèle introduit ci-dessus. En effet, la sélection des caractéristiques permet de donner automatiquement plus d'importance aux caractéristiques qui discriminent le plus et moins d'importance aux caractéristiques qui discriminent le moins. Finalement, dans la cinquième partie, nous proposons un nouveau modèle bayésien d'analyse discriminante logistique construit à l'aide de noyaux permettant ainsi une classification nonlinéaire flexible

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Discriminant analysis model for predicting contractor performance in Hong Kong

This thesis describes the development of an operational research model for the identification of determinating variables and prediction of contractor performance in Hong Kong. The mathematical technique used is the Discriminant Analysis approach. The model is also verified with two other analyses Multiple Regression Analysis and Unidimensional Scaling Analysis. One of the aims of the research is to betray the underlying factors that influence contractor performance which are measured in the clients' point of view. The second aim is to develop an accurate model for predicting contractor performance used by clients in vetting contractors. All aspects of the model's development are described, including the quantification of the variables, data collection, analysis of the model results, verification of the model results with other models and testing the model using independent data. Further, the variables adopted in the model are compared with the actual practices in Hong Kong. The predictive model produced by the study is made up of six variables measuring the three dimensions namely the inherent characteristics of the project, the contractor's internal attributes and the external influence of the project team, including the complexity of the project, the working experience of the project leaders, the percentage of professionally qualified staff in the company, the past performance of the contractor, the origin of the company and the architect's or client's supervision and control on the quality of work and work progress. However, the developed ndels should only be used as part of an assessment process and with caution as there are other unpredictable factors which are not able to quantify and include in the model such as the changing of the ccmpany structure and straty, change in management quality, profitability and the happening of overtrading. Nevertheless, the use of the model to exclude cximpanies fran tender lists could accelerate the contractor selection process and spare niore time for clients to concentrate on more important issues

Classifying by Bayesian Method and Some Applications

This chapter sums up and proposes some results related to classification problem by Bayesian method. We present the classification principle, Bayes error, and establish its relationship with other measures. The determination for Bayes error in reality for one and multi-dimensions is also considered. Based on training set and the object that we need to classify, an algorithm to determine the prior probability that can make to reduce Bayes error is proposed. This algorithm has been performed by the MATLAB procedure that can be applied well with real data. The proposed algorithm is applied in three domains: biology, medicine, and economics through specific problems. With different characteristics of applied data sets, the proposed algorithm always gives the best results in comparison to the existing ones. Furthermore, the examples show the feasibility and potential application in reality of the researched problem

Machine learning algorithms for cognitive radio wireless networks

In this thesis new methods are presented for achieving spectrum sensing in cognitive radio wireless networks. In particular, supervised, semi-supervised and unsupervised machine learning based spectrum sensing algorithms are developed and various techniques to improve their performance are described. Spectrum sensing problem in multi-antenna cognitive radio networks is considered and a novel eigenvalue based feature is proposed which has the capability to enhance the performance of support vector machines algorithms for signal classification. Furthermore, spectrum sensing under multiple primary users condition is studied and a new re-formulation of the sensing task as a multiple class signal detection problem where each class embeds one or more states is presented. Moreover, the error correcting output codes based multi-class support vector machines algorithms is proposed and investigated for solving the multiple class signal detection problem using two different coding strategies. In addition, the performance of parametric classifiers for spectrum sensing under slow fading channel is studied. To address the attendant performance degradation problem, a Kalman filter based channel estimation technique is proposed for tracking the temporally correlated slow fading channel and updating the decision boundary of the classifiers in real time. Simulation studies are included to assess the performance of the proposed schemes. Finally, techniques for improving the quality of the learning features and improving the detection accuracy of sensing algorithms are studied and a novel beamforming based pre-processing technique is presented for feature realization in multi-antenna cognitive radio systems. Furthermore, using the beamformer derived features, new algorithms are developed for multiple hypothesis testing facilitating joint spatio-temporal spectrum sensing. The key performance metrics of the classifiers are evaluated to demonstrate the superiority of the proposed methods in comparison with previously proposed alternatives

Bayesian perspectives on statistical modelling

This thesis explores the representation of probability measures in a coherent Bayesian modelling framework, together with the ensuing characterisation properties of posterior functionals. First, a decision theoretic approach is adopted to provide a unified modelling criterion applicable to assessing prior-likelihood combinations, design matrices, model dimensionality and choice of sample size. The utility structure and associated Bayes risk induces a distance measure, introducing concepts from differential geometry to aid in the interpretation of modelling characteristics. Secondly, analytical and approximate computations for the implementation of the Bayesian paradigm, based on the properties of the class of transformation models, are discussed. Finally, relationships between distance measures (in the form of either a derivative of a Bayes mapping or an induced distance) are explored, with particular reference to the construction of sensitivity measures