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Abstract: Representation of objects by multi-dimensional data arrays has become very common for many research areas e.g. image analysis, signal processing and chemometrics. In most cases, it is the straightforward representation obtained from sophisticated measurement equipments e.g. radar signal processing. Although the use of this complex data structure could be advantageous for a better discrimination between different classes of objects, it is usually ignored. Classification tools that take this structure into account have hardly been developed yet. Meanwhile, the dissimilarity representation has demonstrated advantages in the solution of classification problems e.g. spectral data. Dissimilarities also allow the representation of multi-dimensional objects in a way that the data structure can be used. This paper introduces their use as a tool for classifying objects originally represented by two-dimensional (2D) arrays. 2D measures can be useful to achieve this representation. A 2D measure to compute the dissimilarity representation from spectral data with this kind of structure is proposed. It is compared to existent 2D measures, in terms of the information that is taken into account and computational complexity. [Copyright &y& Elsevier]

In this article, a novel approach to schizophrenia classification using magnetic resonance images (MRI) is proposed. The presented method is based on dissimilarity-based classification techniques applied to morphological MRIs and diffusion-weighted images (DWI). Instead of working with features directly, pairwise dissimilarities between expert delineated regions of interest (ROIs) are considered as representations based on which learning and classification can be performed. Experiments are carried out on a set of 59 patients and 55 controls and several pairwise dissimilarity measurements are analyzed. We demonstrate that significant improvements can be obtained when combining over different ROIs and different dissimilarity measures. We show that combining ROIs using the dissimilarity-based representation, we achieve higher accuracies. The dissimilarity-based representation outperforms the feature-based representation in all cases. Best results are obtained by combining the two modalities. In summary, our contribution is threefold: (i) We introduce the usage of dissimilarity-based classification to schizophrenia detection and show that dissimilarity-based classification achieves better results than normal features, (ii) We use dissimilarity combination to achieve better accuracies when carefully selected ROIs and dissimilarity measures are considered, and (iii) We show that by combining multiple modalities we can achieve even better results. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 179-192, 2011 [ABSTRACT FROM AUTHOR]

Provides information on the development of a theoretical framework for combining classifiers which use distinct pattern representations. Comparison of the different classifier combinations schemes; Investigation of the sensitivity analysis of the schemes; Results of the analysis.

Abstract: This study presents a system for detecting and scoring of a knee disorder, namely, osteoarthritis (OA). Data used for training and recognition is mainly data obtained through computerized gait analysis, which is a numerical representation of the mechanical measurements of human walking patterns. History and clinical characteristics of the subjects such as age, body mass index and pain level are also included in decision-making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren–Lawrence scale: “Normal”, “Mild”, “Moderate”, and “Severe”. Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e. implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. The approach is similar to the Mixture of Experts method. Individual feature selection is applied using the Mahalanobis distance measure and most discriminatory features are used for each expert MLP. The system is tested by a separate set and a success rate of about 80% is achieved on the average. [Copyright &y& Elsevier]

Abstract: Hidden Markov models (HMMs) have been successfully applied to a wide range of sequence modeling problems. In the classification context, one of the simplest approaches is to train a single HMM per class. A test sequence is then assigned to the class whose HMM yields the maximum a posterior (MAP) probability. This generative scenario works well when the models are correctly estimated. However, the results can become poor when improper models are employed, due to the lack of prior knowledge, poor estimates, violated assumptions or insufficient training data. To improve the results in these cases we propose to combine the descriptive strengths of HMMs with discriminative classifiers. This is achieved by training feature-based classifiers in an HMM-induced vector space defined by specific components of individual hidden Markov models. We introduce four major ways of building such vector spaces and study which trained combiners are useful in which context. Moreover, we motivate and discuss the merit of our method in comparison to dynamic kernels, in particular, to the Fisher Kernel approach. [Copyright &y& Elsevier]