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作者： Shuxia LU Limin LI Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Classification for large datasets is a classical problem in machine learning. In this paper, we focus on effevtive classification algorithm for large datasets and imbalanced datasets. First, to deal with imbalanced dataset, we define the weight according to the size of positive and negative dataset. Then, a fast learning algorithm on large datasets called a core set weighted support vector machines(CSWSVM) is proposed. In the proposed approach, the corresponding core set(CS) can be solved by employing the core vector machine(CVM) or generalized CVM(GCVM), and then the weighted support vector machines(WSVM) can be used to implement classification for imbalanced datasets. Experimental results on UCI and USPS datasets demonstrate that the proposed method is effective.

关键词： core vector machine weight support vector machine core set

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A Fast Algorithm for Computing Dominance Classes

A Fast Algorithm for Computing Dominance Classes

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作者： Yan LI Qun YU Key Lab.Of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University

Traditional rough set theory(TRS) is based on the concept of equivalence relation to define upper and lower approximation sets of a given target concept, and therefore uncertainties in information systems can be represented. By using equivalence relations, TRS only considers whether attribute values are distinguished or not, regardless of the preference information contained in attribute values. Rough sets based on dominance relations effectively solve this problem and can deal with preference-ordered data. In these dominance-based approaches, the computational cost of the dominance classes greatly affects the efficiency of attribute reduction and rule extraction. This paper presents an efficient method of computing dominance classes in an ordered information system by rapidly reducing the search space. Based on the definition of dominance class, the inferior class of an object is gradually removed from the universe with the increase of the attributes in the computation process. Experiments on ten UCI data sets show that the proposed algorithm obviously improves the efficiency of computing dominance classes, especially for large-scale data.

关键词： Rough set dominance class ordered information systems fast algorithm

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A Rough Set Model Based on Extended Dominance Relations

A Rough Set Model Based on Extended Dominance Relations

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作者： Yan LI Shuai SUN Yongfei JIN Yan ZHAN Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University

Dominance relation rough set approach(DRSA) is a useful mathematical tool to deal with preference-ordered data. The main idea is using dominance relations to replace equivalent relations in classical rough set theory. However, the definition of conventional dominance relation is very strict which may limit its application to information systems with relative large number of attributes. In this paper, we relax the conditions in the definition of dominance relation and introduce the concept of extended dominance relation. The proprieties of this new concept are also discussed and it is found that all the properties of classical dominance relation are still satisfied.

关键词： Rough Set Information System Dominance Relation DRSA Extended Dominance Relation

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A Multi-task Pathfinding Method Based on CBR and Kd-tree

A Multi-task Pathfinding Method Based on CBR and Kd-tree

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作者： Yan Li Sen Liang Lanming Su Jiacheng He Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Pathfinding is a typical task in many computer games, and its performance will affect the quality of game AI. In order to enhance the efficiency of multi-task pathfinding, case-based reasoning has been introduced in traditional A* algorithm, called the CBMT method. The method needs to select representative paths which can cover the whole map to build a compact case base, which is difficult in large maps. Besides, repeatedly searching for similar cases for each pathfinding task would be a time consuming process. To address these problems, we provide a kd-tree case storage structure and case retrieval mechanical in the CBMT method. The pre-stored cases(previously found paths) are generated randomly and incrementally. The original flat storage structure of the cases is changed into the kd-tree structure. Since the searching space can be reduced by branch pruning in case retrieval, the pathfinding efficiency has been improved obviously, and the number of searched nodes is also reduced.

关键词： Multi-task Pathfinding Case-based Reasoning Kd-tree Nearest-neighbors Search Algorithm A

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A deep web interface integration approach based on domain knowledge and submitting queries

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Journal of Information and computational Science 2009年第3期6卷 1103-1111页

作者： Yuan, Fang Liu, Hongfei Wei, Yonggang Yao, Zengli College of Mathematics and Computer Science Hebei University Hebei Baoding 071002 China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Hebei Baoding 071002 China

Deep Web can provide us a great amount of high quality information. In order to make full use of the information, it is becoming urgent to establish Deep Web data integration system, in which Deep Web interface integration is an important component. This paper proposes a deep web interface integration method based on domain knowledge and submitting queries for certain domains. Before application this method, we categorize Deep Web interfaces according to their structures and introduce the concept of interface element, which be used to represent the interface. This approach first selects the required attributes, and then matches these attributes in each interface element, after that integrates each interface element which matches the same attribute to generate the global interface. This method includes interface template matching, domain knowledge-based interface elements matching and submitting queries-based interface elements matching, etc. Experimental results show that the proposed method has higher matching accuracy and lower dependency on preparatory work. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Domain Knowledge

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A HSC-based sample selection method for Support Vector machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on machine learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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MINING THE HOTTEST TOPICS ON CHINESE WEBPAGE BASED ON THE IMPROVED K-MEANS PARTITIONING

MINING THE HOTTEST TOPICS ON CHINESE WEBPAGE BASED ON THE IM...

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2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： YU WANG YA-HUI XI LIANG WANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science HebeiUniversity Baoding 071002 China

This paper presents a new method for the mining the hottest topics on Chinese webpage which is based on the improved k-means partitioning algorithm. The dictionary applied to word segmentation is reduced by deleting words which are useless for clustering, and the dictionary tree is created to be applied to word segmentation. Then the speed of word segmentation is improved. Correspondence between words and integers is created by coding words. Then the title is expressed by integer set, and the cost of space and time for clustering is decreased largely. Determining the value of k is a shortcoming of stream data mining based on k-means. By this new method, the value of k is adjusted in clustering. Then both the accuracy and the speed are improved.

关键词： k-means partitioning Data stream Word segmentation Mining the hottest topics

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SOME PROPERTIES OF OPERATIONS ON TYPE-2 FUZZY SETS

SOME PROPERTIES OF OPERATIONS ON TYPE-2 FUZZY SETS

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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： SHUO WANG SHU-MING WANG YING LIU Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

In this study, we study set operations on type-2 fuzzy sets. We first discuss join and meet operations of membership grades of type-2 fuzzy sets under left continuous t-norms and derive distributive law of type-2 fuzzy sets. Then, some properties on compositions of fuzzy relations is discussed. We derived that the distributive laws under union and composition of type-2 fuzzy relations is valid. An example shows the failure of distributive laws under intersection and composition.

关键词： Composition Fuzzy relation Operation Type-2 fuzzy set Triangular norm

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APPLICATION OF INTELLIGENT DECISION SUPPORT TECHNOLOGY IN POWER SYSTEM

APPLICATION OF INTELLIGENT DECISION SUPPORT TECHNOLOGY IN PO...

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2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： HAO CHEN YAN ZHAN HAI-YAN LIU Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

Distribution network cabling planning is a very complex project This paper proposes the application of intelligent decision support technology in Power System. By adding a module library and the concept of model management systems, Intelligent Power Service System realizes intelligence decision support in the distribution network power cabling planning by using dynamic programming, spatial data mining and decision tree techniques, and has a certain amount of self-learning ability.

关键词： Intelligent Decision Support System Decision tree Power cabling scheme GIS

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AN INEXACT REASONING ALGORITHM BASED ON INTERACTION WITH FUZZY RULE MATRIX TRANSFORMATION

AN INEXACT REASONING ALGORITHM BASED ON INTERACTION WITH FUZ...

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2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： AI-XIA CHEN NING LI LI ZHAO HONG-TAO ZHU GUO-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

This paper presents a reasoning algorithm based on interaction with fuzzy rule matrix transformation, and applies it to completing the patterns. Then the new full patterns will be used in training and synthetic judgment The investigation shows that the method is effective and may be widely used in Reasoning with Incomplete Knowledge.

关键词： Fuzzy measure Interaction Fuzzy integral Fuzzy rule matriz transformation Inezact reasoning algorithm

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