A novel approach for crossover based on attribute reduction - a case of 0/1 knapsack problem

2009 ◽  
Author(s):  
H. -H. Yang ◽  
S. -W. Wang ◽  
H. -T. Ko ◽  
J. -C. Lin
Keyword(s):  
Knapsack Problem ◽  
Attribute Reduction ◽  
Novel Approach

scholarly journals A Novel Approach for Reducing Attributes and Its Application to Small Enterprise Financing Ability Evaluation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Baofeng Shi ◽  
Bin Meng ◽  
Hufeng Yang ◽  
Jing Wang ◽  
Wenli Shi
Keyword(s):  
Pearson Correlation ◽  
Attribute Reduction ◽  
Multicriteria Decision Making ◽  
Original Data ◽  
Small Enterprise ◽  
Computational Time ◽  
Data Set ◽  
Novel Approach ◽  
Experimental Findings ◽  
Ability Evaluation

Attribute reduction is viewed as a kind of preprocessing steps for reducing large dimensionality in data mining of all complex systems. A great deal of researchers have proposed various approaches to reduce attributes or select key features in multicriteria decision making evaluation. In practice, the existing approaches for attribute reduction focused on improving the classification accuracy or saving the cost of computational time, without considering the influence of the reduction results on the original data set. To help address this gap, we develop an advanced novel attribute reduction approach combining Pearson correlation analysis with F test significance discrimination for the screening and identification of key characteristics related to the original data set. The proposed model has been verified using the financing ability evaluation data of 713 small enterprises of a city commercial bank in China. And the experimental results show that the proposed reduction model is efficient and effective. Moreover, our experimental findings help to locate the qualified partners and alleviate the difficulties faced by enterprises when applying loan.

A novel approach for multi-constraints knapsack problem using cluster particle swarm optimization

2021 ◽  
Vol 96 ◽  
pp. 107399
Author(s):  
R.G. Babukarthik ◽  
Chandramohan Dhasarathan ◽  
Manish Kumar ◽  
Achyut Shankar ◽  
Sanjeev Thakur ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Knapsack Problem ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Novel Approach

A novel approach to attribute reduction based on weighted neighborhood rough sets

2021 ◽  
pp. 106908
Author(s):  
Meng Hu ◽  
Eric C.C. Tsang ◽  
Yanting Guo ◽  
Degang Chen ◽  
Weihua Xu
Keyword(s):  
Rough Sets ◽  
Attribute Reduction ◽  
Novel Approach ◽  
Neighborhood Rough Sets

Approach for Mining Fault Rules of Power Grid Based on the Combination of Rough Set Theory and Association Rule

2013 ◽  
Vol 333-335 ◽  
pp. 693-697
Author(s):  
Yong Chao Liang ◽  
Xi Jia Zhang ◽  
Peng Zhou
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Association Rule ◽  
Rough Set Theory ◽  
Power Grid ◽  
Attribute Reduction ◽  
Novel Approach ◽  
Rules Extraction ◽  
Decision Tables ◽  
Fault Information

With the increasing of fault information transmission capacity in power grid, the volume of information which needs to be concerned by dispatchers has greatly increased, consequently making it difficult to identify the fault signal and analyze the cause of the accident quickly for dispatchers in massive fault information. To settle this problem, this paper uses a novel approach that combines rough set theory with association rule for mining fault rules in a large number of historical fault data of power grid. Firstly, it builds distributed original decision tables according to regions. And then it uses the information entropy algorithm in condition attribute reduction. Lastly, it applies the improved Apriori algorithm of association rule to fault rules mining based on the reduction decision table. In this way the problems of redundancy of massive fault information can be solved and complexity of rules extraction can be simplified effectively. It also improves the efficiency of fault rules mining.

A Novel Approach Based on Rough Conditional Entropy for Attribute Reduction

2014 ◽  
Vol 644-650 ◽  
pp. 1607-1619 ◽  
Author(s):  
Tao Yan ◽  
Chong Zhao Han
Keyword(s):  
Rough Set Theory ◽  
Probabilistic Approach ◽  
Attribute Reduction ◽  
Conditional Entropy ◽  
Attribute Selection ◽  
Data Sets ◽  
Decision Systems ◽  
Novel Approach ◽  
Series Of Experiments ◽  
Decision Tables

Z. Pawlak’s rough set theory has been widely applied in analyzing ordinary information systems and decision tables. While few studies have been conducted on attribute selection problem in incomplete decision systems because of its complexity. Therefore, it is necessary to investigate effective algorithms to tackle this issue. In this paper, In this paper, a new rough conditional entropy based uncertainty measure is introduced to evaluate the significance of subsets of attributes in incomplete decision systems. Moreover, some important properties of rough conditional entropy are derived and three attribute selection approaches are constructed, including an exhaustive approach, a heuristic approach, and a probabilistic approach. In the end, a series of experiments on practical incomplete data sets are carried out to assess the proposed approaches. The final experimental results indicate that two of these approaches perform satisfyingly in the process of attribute selection in incomplete decision systems.

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