As a feature selection method, support vector machines-recursive feature elimination (SVM-
RFE) can remove irrelevance features but don't take redundant features into consideration …

This research proposes a hierarchical feature selection model to reduce the feature space
for pattern classification problems. The proposed model includes three stages: 1) irrelevant …

As datasets continue to increase in size, it is important to select the optimal feature subset
from the original dataset to obtain the best performance in machine learning tasks. Highly …

A novel method of feature selection combined with sample selection is proposed to select
discriminant features in this paper. Based on support vector machine trained on training set …

A noise-aware version of support vector machines is utilized for feature selection in this
paper. Combining this method and sequential backward search (SBS), a new algorithm for …

The feature selection for classification is a very active research field in data mining and
optimization. Its combinatorial nature requires the development of specific techniques (such …

Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually
eliminating unimportant features. RFE has become a popular method for feature selection in …

Feature selection is an important topic in bioinformatics. Defining informative features from
complex high dimensional biological data is critical in disease study, drug development, etc …

SVM-related feature selection has shown to be effective, while feature selection with
transductive SVMs has been less studied. This paper investigates the use of transductive …

In original data, there may exist redundant features, irrelevant features, noisy features
besides informative features. Extracting informative features while eliminating the others is …