Journal Title : International Journal of Modern Trends in Engineering and Science
Author’s Name : N Anitha | S Deepa
Volume 02 Issue 02 Year 2015
ISSN no: 2348-3121
Page no: 11-15
Abstract— Supervised feature selection algorithms require a large amount of labeled training data. As a result, such algorithms provide insufficient information about the structure of the target concept, and can thus fail to identify the relevant features that are discriminative to different classes. On the other hand, unsupervised feature selection algorithms ignore label information and thus may lead to performance deterioration. For all these reasons, the usefulness of semi-supervised feature selection is more adapted and its effectiveness has been demonstrated. To overcome these drawbacks, we are proposing the semi supervised feature selection. In this work, proposed a framework for feature selection based on Partitioning the datasets, Constraint selection, Relevance of Features and redundancy elimination for semi-supervised dimensionality reduction. A new score function was developed to evaluate the relevance of features based on the locally geometrical structure of unlabeled data and the constraint preserving ability of labeled data.
Keywords— Supervised, Unsupervised, Semi supervised, Constraint, Relevance, Redundancy
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