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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Gang Zheng Shiliu Lian Shanling Mou Jia Qi Laboratory of Biological Signal and Intelligent Processing Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Tianjin University of Technology Tianjin CN China

In this paper, we proposed a tunnel morph model for bio-signal waveform in measuring their similarity. Firstly, the formal specifications of bio-signal waveforms are given. And then, a series of model establishing related definitions are presented. These definitions contain waveform segmentation; waveforms distance measurement, and tunnel width computation. Moreover, on the base of the model, a similarity measuring strategy which takes the curve feature of bio-signal into account was presented. In the end, the strategy was compared with other similarity measurement methods by AECG (Ambulatory Electrocardiogram) waveform data. The data are adopted from MIT/BIH arrhythmia database. Experiment results show that the sensitivity and the positive predictivity of the strategy based on tunnel morph model are prior to other strategies.

关键词： Electrocardiography Equations Biological system modeling Correlation Sensitivity Mathematical model

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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 (ICMLC)

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

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.

关键词： Support vector machines Training Classification algorithms Machine learning Accuracy Testing Machine learning algorithms

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Enhanced Interval Approach for encoding words into interval type-2 fuzzy sets and convergence of the word FOUs

Enhanced Interval Approach for encoding words into interval ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Simon Coupland Jerry M. Mendel Dongrui Wu Centre of Computational Intelligence De Montfort University Leicester UK Signal and Image Processing Institute Ming Hsieh Department of Electrical Engineering University of Southern California Los Angeles CA USA Institute of Creative Technologies and the Signal Analysis and Interpretation Laboratory Viterbi School of Engineering University of Southern California Los Angeles CA USA

The Interval Approach (IA) [4] is a method for synthesizing an interval type-2 fuzzy set (IT2 FS) model for a word from data that are collected from a group of subjects. A key assumption made by the IA is: each person's data interval is random and uniformly distributed. This means, of course, that the IT2 FS model for the word is random. Consequently, one can question whether or not the IT2 FS model for the word converges in a stochastic sense. This paper focuses on this question. As a part of our study, we have had to modify some steps of the IA, the resulting being an Enhanced IA (EIA). The paper shows by means of some simulations, that the IT2 FS word models that are obtained from the EIA are converging in a mean-square sense. This provides substantial credence for using the EIA to obtain T2 FS word models.

关键词： Convergence Frequency selective surfaces Mathematical model Stochastic processes Fuzzy sets Probability distribution Context

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Logical Implication of Structural Integrity Constraints for XML

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电子学报(英文版) 2009年第2期18卷 243-248页

作者： ZHANG Jianmei TAO Shiqun LIANG Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan 030006 China Changzhi University Changzhi 046011 China

For tree XML, constraints that specify structural relationships among nodes or paths are very natural. In this paper, we introduce the concept of structural integrity constraints for XML (XSICs), which specify path implication, path cooccurrence, path mutual-exclusion, element obligatory inclusion and exclusive inclusion, and define the syntax and semantics of XSICs. For reasoning about XSICs, we rewrite all the other constraints into path implication constraints, and develop a sound and complete set of inference rules for path implication constraints. Meanwhile, we propose the concept of path implication closure. By using the path implication closure, we prove the completeness of inference rules, and determine the implication decision about XSICs.

关键词：完整性约束推理规则相互排斥 XML 路径道路强制性包容性

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An approach for facial expression recognition based on neural network ensemble

An approach for facial expression recognition based on neura...

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2009 International Conference on Machine Learning and Cybernetics

作者： Bai, Xue-Fei Wang, Wen-Jian School of Computer and Information Technology Shanxi University Key Laboratory of Computational Intelligence and Chinese Information Processing Taiyuan 030006 China

ISBN: (纸本)9781424437030

This paper proposes a novel method for facial expression recognition based on neural network ensemble. The facial expression features are extracted firstly through multi-expression eigenspace analysis, and then several neural networks are trained each with an eigenspace of different expressions respectively. At last their training results are aggregated as inputs of the ensemble classifier, which will provide not only the final recognition results but also the estimated expression information. Simulation results on JAFEE dataset show that the recognition accuracy of the proposed approach is better than that of the best individual neural network. © 2009 IEEE.

关键词： Principal component analysis

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Foundation of granular computing

Foundation of granular computing

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2009 International Conference on Theoretical and Mathematical Foundations of Computer Science, TMFCS 2009

作者： Zhao, Liquan Zhang, Ling Province Key Laboratory of Electronic Business Nanjing University of Finance and Economics Nanjing 210003 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China Artificial Intelligence Institute Anhui University Hefei 230039 China

ISBN: (纸本)9781615676583

Granular computing which imitates the manner of human thinking is the foundation of artificial intelligence. This paper discusses some operations of granules including quotient intersection, quotient union, quotient complement and quotient difference. Then defines a knowledge space and presents it by a group of basis. Besides we introduce granule spaces and their main properties.

关键词： Granular computing

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AN APPROACH FOR FACIAL EXPRESSION RECOGNITION BASED ON NEURAL NETWORK ENSEMBLE

AN APPROACH FOR FACIAL EXPRESSION RECOGNITION BASED ON NEURA...

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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： XUE-FEI BAI WEN-JIAN WANG School of Computer and Information Technology Shanxi University Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Taiyuan 030006 China

关键词： Facial ezpression recognition Neural network ensemble Two-dimension principal component analysis

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An attribute reduction approach and its accelerated version for hybrid data

An attribute reduction approach and its accelerated version ...

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IEEE International Conference on Cognitive Informatics

作者： Wei Wei Jiye Liang Yuhua Qian Feng Wang School of Computer and Information Technology Shanxi University Taiyuan Shanxi China Chinese Information Processing of Ministry of Education Key Laboratory of Computational Intelligence Taiyuan Shanxi China

In practical issues, categorical data and numerical data usually coexist, and a unified data reduction technique for hybrid data is desirable. In this paper, an information measure is proposed for computing the discernibility power of a categorical or numeric attribute. Based on the measure, a uniform definition of significance of attributes with categorical values and numerical values is proposed. Furthermore, an algorithm to obtain an attribute reduct from hybrid data is presented, and one of its accelerated version is also constructed. Experiments show that these two algorithms can get the same reducts, and the classification accuracies of reduced datasets are similar with the ones using Hu's algorithm. However, the accelerated version consumes much less time than the original one and Hu's algorithm do.

关键词： Acceleration Information systems Power measurement Set theory Entropy Fuzzy set theory Argon Fuzzy sets Laboratories computational intelligence

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A NOVEL LEARNING MODEL-KERNEL GRANULAR SUPPORT VECTOR MACHINE

A NOVEL LEARNING MODEL-KERNEL GRANULAR SUPPORT VECTOR MACHIN...

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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： HU-SHENG GUO WEN-JIAN WANG CHANG-QIAN MEN School of Computer and Information Technology Shanxi University Taiyuan 030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Taiyuan 030006 China

This paper presents a novel machine learning model-Kernel Granular Support Vector Machine (KGSVM), which combines traditional support vector machine (SVM) with granular computing theory. By dividing granules and replacing with them in kernel space, the datasets can be reduced effectively without changing data distribution. And then the generalization performance and training efficiency of SVM can be improved. Simulation results on UCI datasets demonstrate that KGSVM is highly scalable for large datasets and very effective in terms of classification.

关键词： Kernel granular support vector machine Support vector machine Kernel space Granules

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Accelerating Incomplete Feature Selection

Accelerating Incomplete Feature Selection

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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： Yuhua Qian Jiye Liang Wei Wei Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Taiyuan 030006 Shanxi China School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi China

Feature selection from incomplete data aims to retain the discriminatory power of original features in rough set theory. Many feature selection algorithms are computationally time-consuming. To overcome this drawback, we introduce a theoretic framework based on rough set theory, called positive approximation, which can be used to accelerate a heuristic process of feature selection from incomplete data. Based on the proposed accelerator, a general feature selection algorithm is designed. Through the use of the accelerator, several representative heuristic feature selection algorithms in rough set theory have been enhanced. Experiments show that these modified algorithms outperform their original counterparts.

关键词： Feature selection Rough set theory Incomplete information systems Positive approzimation Granular computing

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