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Extracting Logical Rules and Minimal Attribute Subset from Confidence Domain
LiMin Wang *
College of Computer Science and Technology, Jilin University
*Correspondence author
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Funding: Postdoctoral Science Foundation（No.20100481053）
Opened online: 2 April 2011
Accepted by: none
Citation: LiMin Wang.Extracting Logical Rules and Minimal Attribute Subset from Confidence Domain[OL]. [ 2 April 2011] http://en.paper.edu.cn/en_releasepaper/content/4419041
 
 
By evaluating the information quantity implicated in training samples, the SLRFD algorithm proposed in this paper divides the training set into two parts, confidence domain and non-confidence domain. The confidence domain is used to translate training samples into logical rules and assign class labels to testing samples in the semi-supervised learning framework. Functional dependency rules of probability are deduced based on Armstrong's axioms to serve the purpose of semantics-preserving data dimensionality reduction for classification without sacrificing the ability to discern between samples belonging to different classes. The attributes corresponding to missing values are proved to be redundant, thus the computational complexity while modeling will be reduced. Empirical studies on a set of natural domains show that SLRFD has clear advantages with respect to generalization and probabilistic performance.
Keywords:Computer Science and Technology; Data Mining; Semi-supervised learning; Missing values; Confidence domain; Functional dependency rules of probability
 
 
 

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