首页|Attribute reduction on measuring data in neighborhood rough set with common-test-cost and error ranges
Attribute reduction on measuring data in neighborhood rough set with common-test-cost and error ranges
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NETL
NSTL
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Many previous studies on measuring data attempted to seek an optimal reduct to achieve a low total test cost,which is based on an assumption that all attributes are independent.In many real-world measurements,however,this assumption is not reasonable due to the affection of some attributes being related.To address this issue,we firstly define decision systems with test-costs,common-test-costs and error ranges for data reduction.The concepts of reduction which include lower and upper approximations,positive regions,and relative reducts are based on neighborhood rough sets.Then we design a heuristic algorithms to attack measuring data sets reduction.In the process of reduction,common-test-costs are used to improve prune strategy and develop information function for algorithm's efficiency.The experiments were conducted on 7 UCI measuring database,which validated the effectiveness of proposed algorithms.
NETL
NSTL
万方数据
