Efficient Feature Subset Selection using Kruskal's Process in Big Data | Abstract

ISSN ONLINE(2320-9801) PRINT (2320-9798)

Research Article Open Access

Efficient Feature Subset Selection using Kruskal's Process in Big Data

Abstract

Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm, FAST, is proposed and experimentally evaluated. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features.

S.Saranya, S.L.Julian Austrina, K.Ravikumar

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