Automatic recognition of sleep spindles in EEG via radial basis support vector machine based on a modified feature selection algorithm

2005 ◽  
Vol 14 (1) ◽  
pp. 56-65 ◽  
Author(s):  
Nurettin Acır ◽  
Cüneyt Güzeliş
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Automatic Recognition ◽  
Support Vector ◽  
Sleep Spindles ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Radial Basis

Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine

2020 ◽  
Vol 58 (3) ◽  
pp. 519-528 ◽  
Author(s):  
Márcio Dias de Lima ◽  
Juliana de Oliveira Roque e Lima ◽  
Rommel M. Barbosa
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Medical Data ◽  
Support Vector ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Data Set ◽  
Bounded Support ◽  
New Feature

A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis

2013 ◽  
Vol 119 (1) ◽  
pp. 159-166 ◽  
Author(s):  
Shuiguang Deng ◽  
Yifei Xu ◽  
Li Li ◽  
Xiaoli Li ◽  
Yong He
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Spectral Analysis ◽  
Support Vector ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

scholarly journals Inspeção Automática de Defeitos em Madeiras de Pinus usando Visão Computacional

2008 ◽  
Vol 15 (2) ◽  
pp. 203-218
Author(s):  
Luiz E. S. Oliveira ◽  
Paulo R. Cavalin ◽  
Alceu S. Britto Jr ◽  
Alessandro L. Koerich
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Defect Detection ◽  
Color Image ◽  
Support Vector ◽  
Grayscale Image ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Vector Machines

This paper addresses the issue of detecting defects in Pine wood using features extracted from grayscale images. The feature set proposed here is based on the concept of texture and it is computed from the co-occurrence matrices. The features provide measures of properties such as smoothness, coarseness, and regularity. Comparative experiments using a color image based feature set extracted from percentile histograms are carried to demonstrate the efficiency of the proposed feature set. Two different learning paradigms, neural networks and support vector machines, and a feature selection algorithm based on multi-objective genetic algorithms were considered in our experiments. The experimental results show that after feature selection, the grayscale image based feature set achieves very competitive performance for the problem of wood defect detection relative to the color image based features.

Artificial bee colony algorithm for feature selection and improved support vector machine for text classification

2019 ◽  
Vol 47 (3) ◽  
pp. 154-170
Author(s):  
Janani Balakumar ◽  
S. Vijayarani Mohan
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Text Classification ◽  
Support Vector ◽  
Data Sets ◽  
Selection Algorithm ◽  
Data Set ◽  
Content Type ◽  
Benchmark Data ◽  
Bee Colony

Purpose Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content. Design/methodology/approach This paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper. Findings The experiment was conducted on real and benchmark data sets. The real data set was collected in the form of documents that were stored in the personal computer, and the benchmark data set was collected from Reuters and 20 Newsgroups corpus. The results prove the performance of the proposed feature selection algorithm by enhancing the text document classification accuracy. Originality/value This paper proposes a new ABCFS algorithm for feature selection, evaluates the efficiency of the ABCFS algorithm and improves the support vector machine. In this paper, the ABCFS algorithm is used to select the features from text (unstructured) documents. Although, there is no text feature selection algorithm in the existing work, the ABCFS algorithm is used to select the data (structured) features. The proposed algorithm will classify the documents automatically based on their content.

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