In this paper, we developed a diagnosis model based on support vector machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our proposed hybrid feature selection method, named improved F -score and Sequential Forward Search (IFSFS), combines the advantages of filter and wrapper methods to select the optimal feature subset from the original feature...

An ensemble of 2D ear matchers is built by training each matcher using a set of Gabor filters and color spaces selected by a genetic algorithm (GA). First, using gray level images, we select the best Gabor filters applying Sequential Forward Floating Selection. Second, using the RGB images, several color spaces are obtained using a GA. Finally, an ensemble of 1-nearest neighbor matchers use the...

Speech emotion recognition is mostly considered in clean speech. In this paper, joint spectro-temporal features (RS features) are extracted from an auditory model and are applied to detect the emotion status of noisy speech. The noisy speech is derived from the Berlin Emotional Speech database with added white and babble noises under various SNR levels. The clean train/noisy test scenario is in...

Intradialytic hypotension (IDH) is a major complication during hemodialysis treatment, and therefore it is highly desirable to identify, at an early stage during treatment, whether the patient is prone to IDH. Heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity (BRS) were analyzed during the first 30 min of treatment to assess information on the autonomic n...

We report classification experiments using the pilot Infant COPE database of neonatal facial expressions. Two sets of DCT coeffiecents were used to train a neural network simultaneous algorithm (NNSOA) and a linear support vector machine (LSVM) to classify neonatal expressions into the two categories of pain and nonpain. In the first set (VAR) only 80 of the coefficients with the highest varian...

Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to e...

Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase an...

A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. This pape...

Intravascular Ultrasound (IVUS) data validation is usually performed by comparing postmortem (ex-vivo) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few amount of available training dataset. In this work we propose a method to include examples from in-vivo labeled plaque to the ex-vivo training dataset...

This paper investigates the classification of different emotional states using speech features from different feature groups. We use both suprasegmental feature groups like pitch, energy, and duration and segmental feature groups like voice quality, zero crossing rate, and articulation. We want to exploit the selection of the most relevant features from these different feature groups to get a b...