NDLI: Optimal sub-reducts in the dynamic environment

Content Provider	IEEE Xplore Digital Library
Author	Fan Min Zhu, W.
Copyright Year	2011
Description	Author affiliation: Lab of Granular Computing, Zhangzhou Normal University, Zhangzhou 363000, China (Fan Min; Zhu, W.)
Abstract	Test cost is what we pay for collecting a data item of an object. It is one of the most important factors in cost-sensitive learning. In many classification applications, there is a test cost constraint due to limited money, time, or other types of resources. Hence we need to construct test sets meeting the constraint, and at the same time, preserving the information of the decision system to the highest degree. This problem has been addressed recently under the framework of cost-sensitive rough sets. In this paper, we consider the dynamic environment where both test costs and the constraint change over time. Whenever such changes take place, optimal test sets should be recomputed. We propose a two-stage approach to this environment. The first stage computes all sub-reducts using a backtrack algorithm. The algorithm is time consuming, however it executes only once. The second stage selects optimal sub-reducts according to test costs and the constraint. It is very efficient and executes every time changes are made. We compare the performance of the new approach with an existing one. Experimental results on four UCI (University of California - Irvine) datasets indicate that the new one is more efficient in the dynamic environment.
Starting Page	457
Ending Page	462
File Size	273396
Page Count	6
File Format	PDF
ISBN	9781457703720
e-ISBN	9781457703713
DOI	10.1109/GRC.2011.6122640
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-08
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Strontium test cost Heuristic algorithms Software algorithms backtrack algorithm cost-sensitive learning Entropy Silicon Vectors attribute reduction constraint Information entropy
Content Type	Text
Resource Type	Article

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