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2009 International Conference on Information Engineering and Computer Science, ICIECS 2009

作者： Li, Xin-Fu Yin, Peng Zheng, Mei Key Lab. in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Institute of International Education Yanshan University Qinhuangdao China

ISBN: (纸本)9781424449941

Ontology mapping has been widely used in ontology application, but the similarity calculation becomes a thorny issue in the process of ontology mapping. In this paper, the different elements of ontology are considered. Semantic similarity and concept similarity are integrated to get the ontology similarity. During the similarity calculation of the concept attributes, filter strategy is used to reduce the number of concept attributes, and a method of attributes similarity calculation is proposed. In addition, comprehensive similarity calculation method is given including the semantic and the concept of ontology. The experiment results show that this approach can improve the precision effectively. ©2009 IEEE.

关键词： Ontology

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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

作者： Zhang, Yu-Fen Zhang, Qun-Feng Yu, Rui-Hua Key Lab. for Machine Learning and Computational Intelligence College of Mathematics and Computer Hebei University Baoding 071002 China Department of Information Technology Baoding University Baoding 071000 China

ISBN: (纸本)9781424465262

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. © 2010 IEEE.

关键词： Random variables

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Discretization of continuous-valued attributes in decision tree generation

Discretization of continuous-valued attributes in decision t...

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International Conference on machine learning and Cybernetics

作者： Li, Wen-Liagn Yu, Rui-Hua Wang, Xi-Zhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China Department of Information Technology Baoding University Baoding 071000 China

ISBN: (纸本)9781424465262

Decision tree is one of the most popular and widely used classification models in machine learning. The discretization of continuous-valued attributes plays an important role in decision tree generation. In this paper, we improve Fayyad's discretization method which uses the average class entropy of candidate partitions to select boundaries for discretization. Our method can reduce the number of candidate boundaries further. Here we also propose a generalized splitting criterion for cut point selection and prove that the cut points are always on boundaries when using this criterion. Along with the formal proof, we present empirical results that the decision trees generated by using such criteria are similar on several datasets from the UCI machine learning Repository. © 2010 IEEE.

关键词： Decision trees

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Three-way decisions model based on rough fuzzy set

Three-way decisions model based on rough fuzzy set

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作者： Zhai, Junhai Zhang, Sufang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China Hebei Branch of China Meteorological Administration Training Centre China Meteorological Administration Baoding China

Three-way decisions model proposed by Yao gives a semantic interpretation of positive region, negative region and boundary region. This model was developed in the framework of classical rough set, the approached target concept is a crisp set, the employed knowledge is a equivalence relation. In this paper, we extend the three-way decisions model to rough fuzzy set. Specifically, the target concept is extended to a fuzzy set, while the used knowledge is also a equivalence relation. An example is given to illustrate the computation processes of the proposed three-way decisions model. The extended model can deal with the problems described by fuzzy decision tables with symbolic-valued conditional attributes and fuzzy decision attributes. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy sets

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A survey of the initialization of centers and widths in radial basis function network for classification

A survey of the initialization of centers and widths in radi...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Dong, Chun-Ru Chan, Patrick P.K. Ng, Wing W.Y. Yeung, Daniel S. Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The radial basis function network (RBFN) has been widely used in various fields such as function regression, pattern recognition, and error detection, etc. However, the structural parameters of RBFN including the number of hidden units, centers vectors, and widths (variances) are one of the most importent issues when training a RBFN, which greatly affect the performance of RBFN. So, the objective of this paper is to construct an elementary survey about this problem. Firstly, the fundamental knowledge and notations of RBFN is introduced. Secondly, we summarize most existing network structure initialization methods for RBFN and categorize them into four goups. Then some typical appraoches for each category are introduced and discussed. The disadvantages and virtues for parts of methods are also introduced. Finally, the paper is concluded with a discussion of current difficulties and possible future directions about RBFN architecture selection. © 2011 IEEE.

关键词： Radial basis function networks

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Integrating local information-based link prediction algorithms with owa operator

Integrating local information-based link prediction algorith...

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3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014

作者： Liu, James N.K. He, Yu-Lin Hu, Yan-Xing Wang, Xi-Zhao Shiu, Simon C.K. Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9789897580185

The objective of link prediction for social network is to estimate the likelihood that a link exists between two nodes x and y. There are some well-known local information-based link prediction algorithms (LILPAs) which have been proposed to handle this essential and crucial problem in the social network analysis. However, they can not adequately consider the so-called local information: The degrees of x and y, the number of common neighbors of nodes x and y, and the degrees of common neighbors of x and y. In other words, not any LILPA takes into account all the local information simultaneously. This limits the performances of LILPAs to a certain degree and leads to the high variability of LILPAs. Thus, in order to make full use of all the local information and obtain a LILPA with highly-predicted capability, an ordered weighted averaging (OWA) operator based link prediction ensemble algorithm (LPEOWA) is proposed by integrating nine different LILPAs with aggregation weights which are determined with maximum entropy method. The final experimental results on benchmark social network datasets show that LPEOWA can obtain higher prediction accuracies which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with nine individual LILPAs. Copyright © 2014 SCITEPRESS.

关键词： Maximum entropy methods

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Potential support vector machine based on the reduced samples

Potential support vector machine based on the reduced sample...

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International Conference on Information Science and Engineering (ICISE)

作者： Shu-xia Lu Gui-en Cao Jie Meng Hua-chao Wang Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding Hebei China

When the training dataset is very large, the learning process of potential support vector machine takes up so large memory that the training speed is very slow. To accelerate the training speed of the potential support vector machine (PSVM) for large-scale datasets, a new method is proposed, which introduces PSVM based on the reduced samples. The new method removes most non-support vectors, and keeps the samples on and near the boundary, which may be the support vectors, as the new training samples. This method is more suitable to large-scale datasets. The experimental results show that the proposed method performs well to decrease the consumption of computer memory, and accelerate the training speed of PSVM.

关键词： Support vector machines Classification algorithms Training Algorithm design and analysis Optimization Accuracy Acceleration

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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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Maximizing bi-mutual information of features for self-supervised deep clustering

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Advances in computational intelligence 2021年第1期2卷 1-11页

作者： Zhao, Jiacheng Chen, Junfen Meng, Xiangjie Zhai, Junhai Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Baoding China

Self-supervised learning based on mutual information makes good use of classification models and lab.l information produced by clustering tasks to train networks parameters, and then updates the downstream clustering assignment with respect to maximizing mutual information between lab.l information. This kind of methods have attracted more and more attention and obtained better progress, but there is still a larger improvement space compared with the methods of supervised learning, especially on the challenge image datasets. To this end, a self-supervised deep clustering method by maximizing mutual information is proposed (bi-MIM-SSC), where deep convolutional network is employed as a feature encoder. The first term is to maximize mutual information between output-feature pairs for importing more semantic meaning to the output features. The second term is to maximize mutual information between an input image and its feature generated by the encoder for keeping the useful information of an original image in latent space as possible. Furthermore, pre-training is carried out to further enhance the representation ability of the encoder, and the auxiliary over-clustering is added in clustering network. The performance of the proposed method bi-MIM-SSC is compared with other clustering methods on the CIFAR10, CIFAR100 and STL10 datasets. Experimental results demonstrate that the proposed bi-MIM-SSC method has better feature representation ability and provide better clustering results.

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A text classification method based on the improved CR-tree and the weighted association rule

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Journal of computational Information Systems 2009年第6期5卷 1577-1584页

作者： Wang, Yu Zhao, Yao Yuan, Fang College of Mathematics and Computer Science Hebei University Baoding 071002 China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding 071002 China

Classification based on association rules is a common and easily understand algorithm for text classification. To improve its classification accuracy, the key is to generate more effective rules. Sometimes, it will overdraw the role of some training texts. To avoiding this and generate more effective rules, this paper defines the Classification Path and proposes a new text classifier (CP-tree) based on CR-tree. When a new text is coming, to avoid overdrawing, association rules are generated through scanning Classification Path;in addition, to make the role of association rules in class prediction more reasonable, the association rules and weight for the new text are obtained not only according to the training texts, but also the new texts. Experimental results show that the accuracy of text classification is improved with the algorithm based on CP-tree. 1553-9105/ Copyright © 2009 Binary Information Press.

关键词： Classification (of information)

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