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1st International Conference on Information Science and Engineering, ICISE2009

作者： Lu, Shuxia Shi, Pu Chen, Ming Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Baoding Pascali Cable TV Integrater Information Network Co. Ltd. Baoding China College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9780769538877

Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of [0, 1]. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.

关键词： Support vector machines

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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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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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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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TWO-PLY ITERATIVE DEEPENING IN CHINESE-CHESS COMPUTER GAME

TWO-PLY ITERATIVE DEEPENING IN CHINESE-CHESS COMPUTER GAME

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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： XI-ZHAO WANG YU-LIN HE PAN SU WEN-LIANG LI Key Lab.for Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

In Chinese-chess computer game (CCCG), a computer player could find the best move for a given board position by using alpha-beta search algorithm. The technique of iterative deepening is an enhancement to alpha-beta search. It is helpful to reduce the size of game tree. In this paper, we improved the prototypical one-ply iterative deepening (OPID) and proposed two-ply iterative deepening (TPID). In game tree searching, we extend the search by two plies from the previous iteration. An iterated series of 2-ply, 4-ply, 6-ply,…searches is carried out. In the experiments, we validate that TPID is feasible and effective. Through applying TPID to minimax search and alpha-beta search respectively, we found that the total number of nodes generated in TPID minimax search and TPID alpha-beta search are all reduced compared with OPID.

关键词： Chinese Chess Computer Game Game Tree One-Ply Iterative Deepening Two-Ply Iterative Deepening Minimaz search Alpha-beta search algorithm

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A NEW HEURISTIC OF THE DECISION TREE INDUCTION

A NEW HEURISTIC OF THE DECISION TREE INDUCTION

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

Decision tree induction is one of the useful approaches for extracting classification knowledge from a set of feature-based instances. The most popular heuristic information used in the decision tree generation is the minimum entropy. This heuristic information has a serious disadvantage-the poor generalization capability [3]. Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. It has good generalization. Considering the relationship between the classification margin of support vector machine(SVM) and the generalization capability, the large margin of SVM can be used as the heuristic information of decision tree, in order to improve its generalization *** paper proposes a decision tree induction algorithm based on large margin heuristic. Comparing with the binary decision tree using the minimum entropy as the heuristic information, the experiments show that the generalization capability has been improved by using the new heuristic.

关键词： Decision tree Support vector machines SMO Clustering large margin Generalization

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A PSO-GD-BASED HYBRID ALGORITHM FOR GENERAL FUZZY MEASURE DETERMINATION

A PSO-GD-BASED HYBRID ALGORITHM FOR GENERAL FUZZY MEASURE DE...

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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： HUAN-YU ZHAO XI-ZHAO WANG Key Lab.for Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called CDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

关键词： Fuzzy measure Fuzzy integral Particle swarm optimization Gradient descent algorithm

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A DYNAMIC FUZZY MEASURE FOR MULTIPLE CLASSIFIER FUSION

A DYNAMIC FUZZY MEASURE FOR MULTIPLE CLASSIFIER FUSION

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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： YA-JING ZHANG XUE-FEI LI JUN-FEN CHEN HUI-MIN FENG College of Science Agricultural University of Hebei Baoding 071001 China Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie

It has been shown that the fuzzy integral is an effective tool for the fusion of multiple classifiers. Of primary importance in the development of the system is the choice of the measure which embodies the importance of subsets of classifiers. In this paper we propose a method for a dynamic fuzzy measure which will change following the pattern to be classified (data dependent). This method uses the neural network which has good study ability. Our experiment results show that this method make the classification accurate improve.

关键词： Multiple classifier Fusion Fuzzy measure Fuzzy integral Neural network

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USING 2-ADDITIVE FUZZY MEASURE IN MULTIPLE CLASSIFIER SYSTEM

USING 2-ADDITIVE FUZZY MEASURE IN MULTIPLE CLASSIFIER SYSTEM

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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 GUO-FANG ZHANG NING LI LI ZHAO GUO-QIANG YUAN Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Department of Basic Courses Hebei College of Finance Baoding 071051 China

Fuzzy measure and integral are widely used in Multiple Classifier System (MCS). But the number of coefficients involved in the fuzzy integral model grows exponentially with the number of classifiers to be aggregated. The main difficulty is to identify all these coefficients. This paper does an attempt Using 2-additrve fuzzy measure in Multiple Classifier System. Our conclusion is that when different interactions exist in different classifiers the complexity of the computation can be significantly reduced by 2-order additive measure.A simple example is included to illustrate the 2-order additive measure.

关键词： Fuzzy measure λ - fuzzy measure 2-additive fuzzy measure Interaction Multiple Classifier System

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