Minimum cost attribute reduction in three-way decisions based Bayesian network

2016 ◽  
Author(s):  
Xiuyi Jia ◽  
Huaxiong Li ◽  
Lin Shang
Keyword(s):  
Bayesian Network ◽  
Attribute Reduction ◽  
Minimum Cost ◽  
Cost Attribute

Minimum cost attribute reduction in decision-theoretic rough set models

2013 ◽  
Vol 219 ◽  
pp. 151-167 ◽  
Author(s):  
Xiuyi Jia ◽  
Wenhe Liao ◽  
Zhenmin Tang ◽  
Lin Shang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Minimum Cost ◽  
Cost Attribute

scholarly journals Fault Troubleshooting Using Bayesian Network and Multicriteria Decision Analysis

2014 ◽  
Vol 6 ◽  
pp. 282013 ◽  
Author(s):  
Yingping Huang ◽  
Yusha Wang ◽  
Renjie Zhang
Keyword(s):  
Decision Analysis ◽  
Bayesian Network ◽  
Minimum Cost ◽  
Multicriteria Decision Analysis ◽  
Diagnostic Model ◽  
Fault Probability ◽  
Multicriteria Decision ◽  
Start Up ◽  
High Efficient ◽  
Standard Values

Fault troubleshooting aims to diagnose and repair faults at the highest efficacy and a minimum cost. The efficacy depends on multiple criteria like fault probability, cost, time, and risk of a repair action. This paper proposes a novel fault troubleshooting approach by combining Bayesian network with multicriteria decision analysis (MCDA). Automobile engine start-up failure is used as a case study. Bayesian network is employed to establish fault diagnostic model for reasoning and calculating standard values of uncertain criteria like fault probability. MCDA is adopted to integrate the influence of the four criteria and calculate utility value of the actions in each troubleshooting step. The approach enables a cost-saving, high efficient, and low risky troubleshooting.

scholarly journals Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zilong Xu ◽  
Hong Zhao ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Information Gain ◽  
Attribute Reduction ◽  
Ant Colony ◽  
Test Cost ◽  
Cost Attribute ◽  
Three Stages ◽  
Filtration Stage

Minimal test cost attribute reduction is an important problem in cost-sensitive learning. Recently, heuristic algorithms including the information gain-based algorithm and the genetic algorithm have been designed for this problem. However, in many cases these algorithms cannot find the optimal solution. In this paper, we develop an ant colony optimization algorithm to tackle this problem. The attribute set is represented as a graph with each vertex corresponding to an attribute and weight of each edge to pheromone. Our algorithm contains three stages, namely, the addition stage, the deletion stage, and the filtration stage. In the addition stage, each ant starts from the initial position and traverses edges probabilistically until the stopping criterion is satisfied. The pheromone of the traveled path is also updated in this process. In the deletion stage, each ant deletes redundant attributes. Two strategies, called the centralized deletion strategy and the distributed deletion strategy, are proposed. Finally, the ant with minimal test cost is selected to construct the reduct in the filtration stage. Experimental results on UCI datasets indicate that the algorithm is significantly better than the information gain-based one. It also outperforms the genetic algorithm on medium-sized dataset Mushroom.

FAST RANDOMIZED ALGORITHM FOR MINIMAL TEST COST ATTRIBUTE REDUCTION

2014 ◽  
Vol 21 (06) ◽  
pp. 1450028
Author(s):  
JINGKUAN LI ◽  
FAN MIN ◽  
WILLIAM ZHU
Keyword(s):  
Heuristic Algorithms ◽  
Information Gain ◽  
Attribute Reduction ◽  
Randomized Algorithm ◽  
Mining Machine ◽  
Test Cost ◽  
Cost Attribute ◽  
Preprocessing Technique ◽  
Two Parameters ◽  
Np Hardness

Attribute reduction is a key data preprocessing technique, and has been widely studied in data mining, machine learning, and granular computing. Minimal test cost attribute reduction is one of important parts researched in cost-sensitive learning. The backtracking algorithm can obtain an optimal reduct, however on only small datasets due to the NP-hardness of the problem. Heuristic algorithms, such as the genetic one and the information gain based one, are employed to deal with this problem. In this paper, we propose the Fast Randomized Algorithm to obtain a satisfactory reduct more efficiently. The focus of the algorithm is a randomization mechanism that deals with attributes addition and deletion. There are two important parameters in the addition stage, namely the selecting probability of attributes and the number of selected attributes per batch. We obtain some appropriate parameter settings through experiments in a variety of datasets. Results show that the optimal settings of two parameters rarely change on different datasets. Our algorithm is more stable and significantly more efficient than existing heuristic ones.

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