Title: Scalable bootstrap attribute reduction for massive data

Authors: Suqin Ji; Hongbo Shi; Yali Lv; Min Guo

Addresses: School of Information Management, Shanxi University of Finance and Economics, Taiyuan, 030031, China ' School of Information Management, Shanxi University of Finance and Economics, Taiyuan, 030031, China ' School of Information Management, Shanxi University of Finance and Economics, Taiyuan, 030031, China ' School of Information Management, Shanxi University of Finance and Economics, Taiyuan, 030031, China

Abstract: Attribute reduction is one of the fundamental techniques for knowledge acquisition in rough set theory. Traditional attribute reduction algorithms have to load the whole dataset into the memory at a time, however, it is unfeasible for attribute reduction of the massive decision table due to hard limitation. To solve this problem, we propose the bag of little bootstraps attribute reduction algorithm (BLBAR), which combines the bag of little bootstraps with attribute discernibility. Specifically, the algorithm first samples from the original decision table to generate a number of decision sub-tables; and then finds the reducts of bootstrap samples of each sub-table through attribute discernibility; finally, all of the reducts are integrated as the reduct of the original massive decision table. Experimental results demonstrate that BLBAR leads to the improved feasibility, scalability and efficiency for attribute reduction on massive decision table.

Keywords: bag of little bootstraps; BLB; attribute reduction; massive data; discernibility of attribute; reduct.

DOI: 10.1504/IJHPCN.2018.096704

International Journal of High Performance Computing and Networking, 2018 Vol.12 No.4, pp.410 - 417

Received: 16 Jun 2016
Accepted: 30 Aug 2016
Published online: 10 Dec 2018 *

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