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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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Update of approximations in ordered information systems under variations of attribute and object set

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Advances in computational intelligence 2022年第1期2卷 1-13页

作者： Li, Yan Wu, Xiaoxue Hua, Qiang Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Baoding China School of Applied Mathematics Beijing Normal University Zhuhai China

Many collected data from real world applications often evolve when new attributes or objects are inserted or old ones are removed. The set approximations of ordered information systems (OIS) need to be updated from time to time for further data reduction, analysis, or decision-making. Incremental approaches are feasible and efficient techniques for updating approaches when any variation occurs. In this paper, considering OIS for multi-criteria classification problems, we discuss the principles of incrementally updating approximations in dominance relation based method in four different types of dynamic environments which combine the changes of both attribute set and object set. In each dynamic environment, the corresponding updating principles and algorithm are given with detail proofs. The experimental results and analysis on UCI data sets show that the proposed incremental approach outperforms the non-incremental method and the integration of current incremental algorithms in the implementation efficiency.

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Markov property of Markov chains and its test

Markov property of Markov chains and its test

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yu-Fen Zhang Qun-Feng Zhang Rui-Hua Yu Key Lab. for Machine Learning and Computational Intelligence Hebei University Baoding China Department of Information Technology Baoding University Baoding China

Markov chains, with Markov property as its essence, are widely used in the fields such as information theory, automatic control, communication techniques, genetics, computer sciences, economic administration, education administration, and market forecasts. While using Markov chains to predict the future events, we must test the Markov property of random variable sequences of the past statistic data. Only when the random variable sequences satisfy the Markov property, can the prediction could be precise. This paper discusses the concept of Markov property and its features, studies its test method, and by example demonstrates the effectiveness of this prediction method.

关键词： Markov processes Random variables machine learning Biological system modeling Prediction methods Water resources

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Ensemble online sequential extreme learning machine for large data set classification

Ensemble online sequential extreme learning machine for larg...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Junhai ZHAI Jinggeng WANG Xizhao WANG College of Mathematics and Computer Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Computer Science Hebei University

ISBN: (纸本)9781479938414

Online sequential extreme learning machine (OS-ELM) proposed by Liang et al. employ sequential learning strategy to learn the target concept from the data. Compared with the original ELM, OS-ELM can learn data one-by-one or chunk-by-chunk with fixed or varying chunk size with almost same performance as ELM. While compared with other state-of-the-art sequential algorithms such as SGBP, RAN and GAP-RBF, OS-ELM has faster learning speed and better generalization ability. However, similar to ELM, OS-ELM also has instability in different trials of simulations. In addition, for large data sets, OS-ELM will not halt when there are training samples not be learned, this phenomenon results in long learning time. In order to deal with the problems, this paper proposes an algorithm named E-OS-ELM for integrating OS-ELM to classify large data sets. The experimental results show that the proposed method is effective and efficient;it can effectively overcome the drawbacks mentioned above.

关键词： Extreme learning machine Ensemble Sequential learning Large data set

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ON THE APPLICATION OF ROUGH SETS TO DATA MINING IN ECONOMIC PRACTICE

ON THE APPLICATION OF ROUGH SETS TO DATA MINING IN ECONOMIC ...

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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： QUN-FENG ZHANG SU-YUN ZHAO YUN-CHAO BAI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie College of Economics Hebei University Baoding 071002 China

Mathematical models play an important role in the studies of modern economics. But in many fields of economics, it is difficult to build mathematical models for complex phenomena. So data mining is getting more and more popular in discovering the potential pattern of economic knowledge from databases. As a powerful tool for data mining, rough set theory has been widely used. In this research, we draw guidelines from several cases of rough set application in economic practice. Furthermore, to avoid the drawbacks of the existing methods, we develop a methodology for rough analysis in economic sector by combining the advantages of the fuzzy variable precision rough set model.

关键词： Data mining Rough sets Reducts Fuzzy variable precision rough sets

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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 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 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 Feature Extraction Method Using Base Phrase and keyword In Chinese Text

A Feature Extraction Method Using Base Phrase and keyword In...

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2008 3rd International Conference on Intelligent System and Knowledge Engineering(第三届智能系统与知识工程国际会议)(ISKE 2008)

作者： Xin-fu LI lei-lei ZHAO li-hong WU College of Mathematics and Computer Science Hebei UniversityBaoding071002HebeiChina Key Lab of Machine Learning and Computation Intelligence of Hebei Province

The feature extraction is the most key technologyof text *** word is used as the feature in the traditional text classification,and its effect forthe text classification is *** featureextraction method using base phrase and keywordchanges the feature extraction of Chinese text fromsyntax and semantic *** the first,analyzing thefeature of baseNP and basedVP,and then make somewords into baseNP and baseVP which accord to therules of phrase,give WSD to other words in the *** paper proposes a stepwise feature extraction fromword to *** experiment results show that thismethod can perform much better than traditionalfeature extraction method it can improve the textclassification precision and recall.

关键词： Feature extraction Text categorization Frequency Support vector machines Support vector machine classification Intelligent systems learning systems Competitive intelligence Knowledge engineering Tagging

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