Call For Paper Volume:7 Issue:9 Sep'2020 |

Feature selection method for High Dimensional Data

Publication Date : 05/11/2016

DOI : 10.21884/IJMTER.2016.3102.BYVXE

Author(s) :

Swati Vishnu Jadhav , Vishwakarma Pinki.

Volume/Issue :
Volume 3
Issue 10
(11 - 2016)

Abstract :

Feature selection is the process of 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 methods. In the second step, the most representative feature that is strongly related to clustering target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. The Minimum-Spanning Tree (MST) using Prim’s algorithm can concentrate on one tree at a time. To ensure the efficiency of FAST, adopt the efficient MST using the Kruskal’s Algorithm clustering method. Display the graph with respect to time showing comparison between Prim’s and Kruskal’s algorithm.

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Feature selection method for High Dimensional Data

November 1, 2016