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Dimensionality Reduction by Feature Co-Occurrence based Rough Set

Volume 15, Number 1, January 2019, pp. 307-316
DOI: 10.23940/ijpe.19.01.p31.307316

Lei Laa, Qimin Caob, and Ning Xub

aSchool of Information Technology & Management, University of International Business and Economics, Beijing, 100029, China
bLibrary, China University of Political Science and Law, Beijing, 100088, China

(Submitted on October 23, 2018; Revised on November 20, 2018; Accepted on December 28, 2018)

Abstract:

Feature selection is the key issue of unstructured data mining related fields. This paper presents a dimensionality reduction method which uses a rough set as the feature selection tool. Different from previous rough set based classification algorithm, it takes feature co-occurrence into account when make attribution reduction to get a more accurate feature subset. The novel method called Feature Co-occurrence Quick Reduction algorithm is in this article. Experimental results show it has a high efficiency in dimensionality reduction—time consumption by approximately 23% less than traditional rough set based dimensionality reduction methods. Moreover, classification based on the feature set selected by Feature Co-occurrence Quick Reduction algorithm is more precise. The proposed algorithm is helpful to us for refining knowledge from massive unstructured data.

 

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