Test-cost-sensitive attribute reduction

2011 ◽  
Vol 181 (22) ◽  
pp. 4928-4942 ◽  
Author(s):  
Fan Min ◽  
Huaping He ◽  
Yuhua Qian ◽  
William Zhu
Keyword(s):  
Attribute Reduction ◽  
Test Cost ◽  
Sensitive Attribute

A set-cover-based approach for the test-cost-sensitive attribute reduction problem

2016 ◽  
Vol 21 (20) ◽  
pp. 6159-6173 ◽  
Author(s):  
Anhui Tan ◽  
Weizhi Wu ◽  
Yuzhi Tao
Keyword(s):  
Attribute Reduction ◽  
Set Cover ◽  
Test Cost ◽  
Reduction Problem ◽  
Sensitive Attribute

A novel test-cost-sensitive attribute reduction approach using the binary bat algorithm

2019 ◽  
Vol 186 ◽  
pp. 104938 ◽  
Author(s):  
Xiaojun Xie ◽  
Xiaolin Qin ◽  
Qian Zhou ◽  
Yanghao Zhou ◽  
Tong Zhang ◽  
...  
Keyword(s):  
Attribute Reduction ◽  
Bat Algorithm ◽  
Test Cost ◽  
Sensitive Attribute ◽  
Reduction Approach

scholarly journals Test-Cost-Sensitive Attribute Reduction of Data with Normal Distribution Measurement Errors

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Hong Zhao ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Measurement Error ◽  
Normal Distribution ◽  
Uniform Distribution ◽  
Rough Set ◽  
Measurement Errors ◽  
Attribute Reduction ◽  
Distribution Measurement ◽  
Test Cost ◽  
Reduction Problem ◽  
Sensitive Attribute

The measurement error with normal distribution is universal in applications. Generally, smaller measurement error requires better instrument and higher test cost. In decision making, we will select an attribute subset with appropriate measurement error to minimize the total test cost. Recently, error-range-based covering rough set with uniform distribution error was proposed to investigate this issue. However, the measurement errors satisfy normal distribution instead of uniform distribution which is rather simple for most applications. In this paper, we introduce normal distribution measurement errors to covering-based rough set model and deal with test-cost-sensitive attribute reduction problem in this new model. The major contributions of this paper are fourfold. First, we build a new data model based on normal distribution measurement errors. Second, the covering-based rough set model with measurement errors is constructed through the “3-sigma” rule of normal distribution. With this model, coverings are constructed from data rather than assigned by users. Third, the test-cost-sensitive attribute reduction problem is redefined on this covering-based rough set. Fourth, a heuristic algorithm is proposed to deal with this problem. The experimental results show that the algorithm is more effective and efficient than the existing one. This study suggests new research trends concerning cost-sensitive learning.

scholarly journals Cost-Sensitive Attribute Reduction in Decision-Theoretic Rough Set Models

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shujiao Liao ◽  
Qingxin Zhu ◽  
Fan Min
Keyword(s):  
Rough Set ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Attribute Reduction ◽  
Test Cost ◽  
Reduction Problem ◽  
Sensitive Attribute ◽  
Definition Of ◽  
Insight Into ◽  
Decision Cost

In recent years, the theory of decision-theoretic rough set and its applications have been studied, including the attribute reduction problem. However, most researchers only focus on decision cost instead of test cost. In this paper, we study the attribute reduction problem with both types of costs in decision-theoretic rough set models. A new definition of attribute reduct is given, and the attribute reduction is formulated as an optimization problem, which aims to minimize the total cost of classification. Then both backtracking and heuristic algorithms to the new problem are proposed. The algorithms are tested on four UCI (University of California, Irvine) datasets. Experimental results manifest the efficiency and the effectiveness of both algorithms. This study provides a new insight into the attribute reduction problem in decision-theoretic rough set models.

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