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 ...

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...

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...

In this paper, we present a novel approach for the classification of zoom-endoscopy images based on the pit-pattern classification scheme. Our feature generation step is based on the computation of a set of statistical features in the wavelet-domain. In the classification step, we employ a one-against-one approach using 1-Nearest Neighbor classifiers together with sequential forward feature sel...

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...

The identification of image acquisition sources is an important problem in digital image forensics. This paper introduces a new feature-based method for digital camera identification. The method, which is based on an analysis of the imaging pipeline and digital camera processing operations, employs bi-coherence and wavelet coefficient features extracted from digital images. The sequential forwa...

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indi...

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The proposed hybrid approaches provide the possibility of efficiently applying any subset ...

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. This paper proposes an impro...

Feature selection methods are usually evaluated by wrapping specific classifiers and datasets in the evaluation process, resulting very often in unfair comparisons between methods. In this work, we develop a theoretical framework that allows obtaining the true feature ordering of two-dimensional sequential forward feature selection methods based on mutual information, which is independent of en...