Ajalmar R. da Rocha Neto

Diagnostic of pathology on the vertebral column with embedded reject option

Computer aided diagnosis systems with the capability of automatically decide if a patient has or not a pathology and to hold the decision on the dificult cases, are becoming more frequent. The latter are afterwards reviewed by an expert reducing therefore time consuption on behalf of the expert. The number of cases to review depends on the cost of erring the diagnosis. In this work we analyse the incorporation of the option to hold a decision on the diagnostic of pathologies on the vertebral column. A comparison with several state of the art techniques is performed. We conclude by showing that the use of the reject option techniques is an asset in line with the current view of the research community.

WCI 04 On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis

This paper reports results from a comprehensive performance comparison among standalone machine learning algorithms (SVM, MLP and GRNN) and their combinations in ensembles of classifiers when applied to a medical diagnosis problem in the field of orthopedics. All the aforementioned learning strategies, which currently comprises the classification module of the SINPATCO platform, are evaluated according to their ability in discriminating patients as belonging to one out of three categories: normal, disk hernia and spondylolisthesis. Confusion matrices of all learning algorithms are also reported, as well as a study of the effect of diversity in the design of the ensembles. The obtained results clearly indicate that the ensembles of classifiers have better generalization performance than standalone classifiers.

Novel approaches using evolutionary computation for sparse least square support vector machines

This paper introduces two new approaches to building sparse least square support vector machines (LSSVM) based on genetic algorithms (GAs) for classification tasks. LSSVM classifiers are an alternative to SVM ones because the training process of LSSVM classifiers only requires to solve a linear equation system instead of a quadratic programming optimization problem. However, the absence of sparseness in the Lagrange multiplier vector (i.e. the solution) is a significant problem for the effective use of these classifiers. In order to overcome this lack of sparseness, we propose both single and multi-objective GA approaches to leave a few support vectors out of the solution without affecting the classifiers accuracy and even improving it. The main idea is to leave out outliers, non-relevant patterns or those ones which can be corrupted with noise and thus prevent classifiers to achieve higher accuracies along with a reduced set of support vectors. Differently from previous works, genetic algorithms are used in this work to obtain sparseness not to find out the optimal values of the LSSVM hyper-parameters.

A new pruning method for extreme learning machines via genetic algorithms

Graphical abstractDisplay Omitted HighlightsWe propose a pruning method for ELM using genetic algorithms (GA).Our proposal estimates the leave-one-out (LOO) error using the PRESS statistic.Our proposal, called GAP-ELM, was tested on 7 real world datasets.GAP-ELM was compared with MLP and RBF neural networks and showed competitive results. Extreme learning machine (ELM) is a recently proposed learning algorithm for single hidden layer feedfoward neural networks (SLFN) that achieved remarkable performances in various applications. In ELM, the hidden neurons are randomly assigned and the output layer weights are learned in a single step using the Moore-Penrose generalized inverse. This approach results in a fast learning neural network algorithm with a single hyperparameter (the number of hidden neurons). Despite the aforementioned advantages, using ELM can result in models with a large number of hidden neurons and this can lead to poor generalization. To overcome this drawback, we propose a novel method to prune hidden layer neurons based on genetic algorithms (GA). The proposed approach, referred as GAP-ELM, selects subset of the hidden neurons to optimize a multiobjective fitness function that defines a compromise between accuracy and the number of pruned neurons. The performance of GAP-ELM is assessed on several real world datasets and compared to other SLFN and a well known pruning method called Optimally Pruned ELM (OP-ELM). On the basis of our experiments, we can state that GAP-ELM is a valid alternative for classification tasks.

Opposite Maps: Vector Quantization Algorithms for Building Reduced-Set SVM and LSSVM Classifiers

A novel method, called Opposite Maps, is introduced with the aim of generating reduced sets for efficiently training of support vector machines (SVM) and least squares support vector machines (LSSVM) classifiers. The main idea behind the proposed approach is to train two vector quantization (VQ) algorithms (one for each class,

Sparse Least Squares Support Vector Machines via Genetic Algorithms

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Classification with reject option for software defect prediction

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