Feature selection has become essential in classification problems with numerous features. This process involves removing redundant, noisy, and negatively impacting features from the dataset to enhance classifier’s performance. Some are less useful than others or do not correlate system’s evaluation, their removal does affect In most cases, a monotonically decreasing impact on performance increa...

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accura...

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...

A new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images is proposed. The proposed method uses Wavelets Transform (WT) as input module to Genetic Algorithm (GA) and Support Vector Machine (SVM). It segregates MR brain images into normal and abnormal. This contribution employs genetic algorithm for feature selection which requires much lighter compu...

In this paper we suggest feature selection and Principal Component Analysis as a way to analyze and compare corpora of emotional speech. To this end, a fast improvement of the Sequential Forward Floating Search algorithm is introduced, and subsequently extensive tests are run on a selection of French emotional language resources well suited for a first impression on general applicability. Tools...

In our previous work, we have developed the backward feature selection method based on class regions approximated by ellipsoids. In this paper, we accelerate feature selection by the forward selection search, the symmetric Cholesky factorization, and deletion of duplicated calculations between consecutive factorizations. The feature selection for two data sets shows that our method is faster th...

Feature extraction is the process of deriving new weakly correlated features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allows higher classification accuracy. The selection and quality of the features representing each pattern have considerable bearing on the success of subsequent pattern classification. In this paper, we s...

This paper presents an activity recording (AR) system and a radial-basis-function-network-based (RBFNB) energy expenditure regression algorithm. The AR system includes motion sensors and an electrocardiogram sensor which is composed of a set of sensor modules (accelerometers and electrocardiogram amplifying/filtering circuits), a MCU module (microcontroller), a wireless communication module (a ...

The feature selection problem in the field of classification consists of obtaining a subset of variables to optimally realize the task without taking into account the remainder variables. This work presents how the search for this subset is performed using the Scatter Search metaheuristic and is compared with two traditional strategies in the literature: the Forward Sequential Selection (FSS) a...