Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and redundant features as possible. This is because irrelevant features do not contribute to the predictive accuracy and redundant features do not redound to getting a better predictor for that they provide mostly information which is already present in other feature(s). The many feature subset ...

As a feature selection method, support vector machinesrecursive feature elimination (SVM-RFE) can remove irrelevance features but don’t take redundant features into consideration. In this paper, it is shown why this method can’t remove redundant features and an improved technique is presented. Correlation coefficient is introduced to measure the redundancy in the selected subset with SVM-RFE. T...

Workspace analysis is one of the most important issues in the robotic parallel manipulator design. ‎However, ‎the unidirectional constraint imposed by cables causes this analysis more challenging in the cabledriven redundant parallel manipulators. Controllable workspace is one of the general workspace in the cabledriven redundant parallel manipulators due to the dependency on geometry parameter...

The performance of association rule based classification is notably deteriorated with the existence of irrelevant and redundant features and complex attributes. Association rules naturally often suffer from a large volume of rules generated, many of which are not interesting and useful. Thus, selecting relevant feature and/or removing unrelated rules can significantly improve the association ru...

High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough se...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Feature Selection (FS) is applied to reduce the number of features in many applications where data has multiple features. FS is an essential step in successful data mining applications, which can effectively reduce data dimensionality by removing t...

In many cases, redundant systems are beset by both independent and dependent failures. Ignoring dependent variables in MTBF evaluation of redundant systems hastens the occurrence of failure, causing it to take place before the expected time, hence decreasing safety and creating irreversible damages. Common cause failure (CCF) and cascading failure are two varieties of dependent failures, both l...