The problem of determining optimal decision model is a difficult combinatorial task in the
fields of pattern classification and machine learning. In this paper, we propose a new …

As a supervised learning algorithm, Support Vector Machine (SVM) is very popularly used
for classification. However, the traditional SVM is error-prone because of easy to fall into …

Feature selection aims to choose a feature subset that has the most discriminative
information from the original feature set. In practical cases, it is preferable to select a feature …

For the classification problems based on support vector machine, if the sample contains
irrelative or even completely irrelative features to the problem, the difference related to the …

Feature selection refers to a problem to select a subset of features which are most optimal
for intended tasks. As one of well-known feature selection methods, clustering features into …

This article investigates the performance of combining support vector machines (SVM) and
various feature selection strategies. Some of them are filter-type approaches: general …

Conventional classifiers do not have a mechanism to control class boundaries. Thus if the
number of input variables is large compared to the number of training data, class boundaries …

Feature selection refers to a critical preprocessing of machine learning to remove irrelevant
and redundant data. According to feature selection methods, sufficient samples are usually …

A feature selection method based on mutual information and support vector machine (SVM)
is proposed in order to eliminate redundant feature and improve classification accuracy …

Feature selection is an important prerequisite of any pattern recognition, machine learning
or data mining problem. A lot of algorithms for feature subset selection have been developed …