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	1. Extracting Logical Rules and Minimal Attribute Subset from Confidence Domain
	LiMin Wang
	Computer Science and Technology 29 March 2011
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	Abstract: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.
	TO cite this article:LiMin Wang. Extracting Logical Rules and Minimal Attribute Subset from Confidence Domain[OL].[29 March 2011] http://en.paper.edu.cn/en_releasepaper/content/4419041


	2. The Expected First Hitting Time of a class of Gene Expression Programming
	Du Xin ,Ding Lixin
	Computer Science and Technology 20 July 2010
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	Abstract:This paper studies the time complexity of gene expression programming based on maintaining elitist (ME-GEP). By using the theory of Markov Chain and the technique of Artificial fitness level, the upper and lower bounds of the expected first hitting time(EFHT) of ME-GEP are obtained. Furthermore, the unknown parameter of the upper bound is estimated, which is determined by the parameters of ME-GEP algorithm. As an application of the theoretical results acquired in the paper, the EFHT of ME-GEP for solving the polynomial function modeling problem are analyzed, which verifies the relations between the EFHT and the parameters of ME-GEP algorithm. Finally, our theoretical result is used in directing the parameter design of ME-GEP algorithm. And the experiments of solving the above polynomial function modeling problem verify the correctness of our theoretical result.
	TO cite this article:Du Xin ,Ding Lixin . The Expected First Hitting Time of a class of Gene Expression Programming[OL].[20 July 2010] http://en.paper.edu.cn/en_releasepaper/content/4379501


	3. Knowledge, Knowledge Granulation in Information System
	Jiye Liang,Yuhua Qian
	Computer Science and Technology 10 April 2006
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	Abstract:Granular computing is potentially used in knowledge discovery and data mining etc. Based on information system, the extent of closeness and difference between knowledge is measured by introducing the concepts of knowledge closeness and knowledge distance, the axiom definition of knowledge granulation is given, and several knowledge granulations are all special form under the definition. These results will be very helpful for understanding the essence of the granulation, and have important instructive significance for establishing granular computing in information system.
	TO cite this article:Jiye Liang,Yuhua Qian. Knowledge, Knowledge Granulation in Information System[OL].[10 April 2006] http://en.paper.edu.cn/en_releasepaper/content/6154


	4. A New View of Learning Machine
	Maozhi Hu
	Computer Science and Technology 25 November 2005
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	Abstract:for the development of LM.SVM is a new type of LM, which is base on SLT. It is widely used in mode recognize because of its better performance in learning capacity, This paper focuses on the comparison between SVM(a type of LM) and human brain and predicts bravely the future of SVM.
	TO cite this article:Maozhi Hu. A New View of Learning Machine[OL].[25 November 2005] http://en.paper.edu.cn/en_releasepaper/content/3784


	5. A Unified Subspace Outlier Ensemble Framework for Outlier Detection
	zengyou he,Xiaofei Xu
	Computer Science and Technology 25 May 2005
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	Abstract:he task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most such applications are high dimensional domains in which the data may contain hundreds of dimensions. However, the outlier detection problem itself is not well defined and none of the existing definitions are widely accepted, especially in high dimensional space. In this paper, our first contribution is to propose a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. In our new framework, the outlying-ness of each data object is measured by fusing outlier factors in different subspaces using a combination function. Accordingly, we show that all existing researches on outlier detection can be regarded as special cases in the unified framework with respect to the set of subspaces consider
	TO cite this article:zengyou he,Xiaofei Xu. A Unified Subspace Outlier Ensemble Framework for Outlier Detection[OL].[25 May 2005] http://en.paper.edu.cn/en_releasepaper/content/2107

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