In this paper, we propose a new information distance to measure the relevancy of two features. Unlike the information measure in previous feature selection ...
Feature selection is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy and efficiency. In this paper, wepropose a new ...
Feature selection is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy and efficiency. In this paper, wepropose a new ...
Feature selection is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy and efficiency. In this paper, we propose a ...
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The methods based on F-test estimate the degree of linear dependency between two random variables. On the other hand, mutual information methods can capture ...
Abstract—Feature selection tries to find a subset of feature from a larger feature pool and the selected subset can provide.
Typically, they measure the correlation of each feature with the class label by using distance, information or dependence measures [4]. Obviously, the absence ...
Abstract—The quality of the data being analyzed is a critical factor that affects the accuracy of data mining algorithms. There are two.
This paper proposes a subset selection algorithm for supervised classification which targets at finding features that can best predict class labels that ...
For feature selection, RST provides a positive region-based dependency measure called “attribute dependency” to perform feature selection. Attribute dependency ...