Feature Selection using Fuzzy Support Vector Machines

Authors:
Hong Xia;Bao Qing Hu
Affiliations:
School of Mathematics and Statistics, Wuhan University, Wuhan, China;School of Mathematics and Statistics, Wuhan University, Wuhan, China
Venue:
Fuzzy Optimization and Decision Making
Year:
2006

Citing 9
Cited 4

The nature of statistical learning theory

The nature of statistical learning theory
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Comparison of Classifier-Specific Feature Selection Algorithms

Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
An introduction to variable and feature selection

The Journal of Machine Learning Research
Training algorithms for fuzzy support vector machines with noisy data

Pattern Recognition Letters
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Fuzzy support vector machines

IEEE Transactions on Neural Networks

Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data

International Journal of Approximate Reasoning
FSVM-CIL: fuzzy support vector machines for class imbalance learning

IEEE Transactions on Fuzzy Systems - Special section on computing with words
A novel feature selection method based on normalized mutual information

Applied Intelligence
Robust classifier learning with fuzzy class labels for large-margin support vector machines

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.