Toward Accurate Feature Selection Based on BSS-GRF

Toward Optimal Feature Selection

In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In pa...

Toward Suboptimal Feature Selection

Feature selection can be defined as the problem of finding the optimal subset of features that meet some criterion, but in general it is an ill-posed problem. In practice, the only way to compare feature selection algorithms has been through generalization error. For this reason it is extremely rare to see optimality discussed in the context of feature selection. The first ones to make optimali...

Stable and Accurate Feature Selection

In addition to accuracy, stability, having not too significant changes in the selected features when the identity of samples change, is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high. In this study, we introduce a stability measure that can be used for the case ...

Ant Colony Optimization Toward Feature Selection

Fast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...