In this paper, two strategies to compute the support sets system for the supervised classifier ALVOT (voting algorithms) using sequential floating selection are presented. ALVOT is a supervised classification model based on the partial precedence principle, therefore, it needs, as feature selection, a set of features subsets, this set is called support sets system. The sequential floating selec...

In this paper we describe feature selection experiments for on-line handwriting recognition. We performed a sequential forward search through a 25 elements set of on-line and pseudo-off-line features. In our experiments we obtained interesting results. Using a set of only five features, we achieved a performance that is similar that of the reference system that uses all features. The best subse...

This paper provides a comparative study on the different techniques of classifying human activities that are performed using bodyworn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector ...

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, inc...

It is often difficult to come up with a well-principled approach to the selection of low-level features for characterizing images for content-based retrieval. This is particularly true for medical imagery, where gross characterizations on the basis of color and other global properties do not work. An alternative for medical imagery consists of the “scattershot” approach that first extracts a la...

In this paper, we propose a hybrid filter/wrapper approach for fast feature selection using the Residual Mutual Information (RMI) between the function approximator output and the remaining features as selection criterion. This approach can handle redundancies in the data as well as the bias of the employed learning machine while keeping the number of required training and evaluation procedures ...

The problem of feature selection for Support Vector Machines (SVMs) classification is investigated in the linear two classes case. We suggest a new method of feature selection based on ranking scores derived from SVMs. We analyze the retraining effects on the ranking rules based on these scores. Our features selection algorithm consists in a forward selection strategy according to the decreasin...

Accurate grading for hepatocellular carcinoma (HCC) in biopsy images is important to prognosis and treatment planning. However, visual grading is always time-consuming, subjective, and inconsistent. In this paper, we proposed a novel approach to automatically classifying biopsy images into five grades. At first, a dual morphological reconstruction method was applied to remove noise and accentua...

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emot...