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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Fei-Jiang Li Yu-Hua Qian Jie-Ting Wang Ji-Ye Liang 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

ISBN: (纸本)9781467372220

As an important reflection of human cognitive ability, the multi-granulation analysis gets more reasonable solution of a problem in comparison to the single granulation. Clustering analysis is an active area of machine learning and a fundamental technique of information granulation. By using different clustering algorithms and different parameters of an algorithm, a data set can be granulated into multiple granular spaces. Clustering ensemble with these granular spaces is an effective strategy of multigranulation information fusion. The existing algorithms of clustering ensemble can be categorized into three types: feature-based method, combinatorial method and graph-based method. Given the fact that every type of methods has their own advantages and disadvantages, combining their advantages will obtain better granulation results. Based on this consideration, this paper introduces a Dempster-Shafer evidence theory based clustering ensemble method that combines advantages of combinatorial method and graph-based method. In this strategy, the definition of mass functions considers neighbors of an object using the graph binarization and the final clustering ensemble result is generated by applying the Dempster's combination rule. The form of the Dempster's combination rule makes the algorithm conforming to the pattern of combinatorial method. Experimental results show that the proposed method yields better performance in comparison with other seven clustering ensemble methods conducted on fourteen numerical real-world data sets from the UCI Machine Learning Repository.

关键词： Clustering ensemble Dempster-Shafer evidence theory Information fusion Multigranulation Information fusion combinatorial method Evidence Theory Dempster and Shafer's theory Cluster Analysis Clustering algorithms granulation

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Improved low energy adaptive clustering hierarchy protocol based on local centroid bat algorithm

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Sensor Letters 2014年第9期12卷 1372-1377页

作者： Cao, Yang Cui, Zhihua Li, Feixiang Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Electric Engineering Southwest Jiaotong University Sichuan China

Bat algorithm (BA) is a novel intelligent optimization algorithm. In order to improve the poor local search capability of the algorithm, we present a new variant of bat optimization algorithm based on local centroid strategy (LCBA). Meanwhile, LEACH is a low energy adaptive clustering hierarchy algorithm for wireless sensor networks (WSN). And it has many disadvantages such as random selection of cluster head, taking no account of the remaining energy and position of nodes. To solve these problems, this paper proposes an improved LEACH protocol based on local centroid bat algorithm. The improved protocol divides the cluster head selection process into optimization of temporary cluster head and formal cluster head selection. First, we generate temporary cluster heads by traditional LEACH protocol, then optimize these cluster heads based on LCBA and select formal cluster heads according to the remaining energy of nodes. The result of experiment shows that, comparing with LEACH, LEACH-LCBA can balance the network load efficiently, improve the energy utilization, prolong the network lifetime.

关键词： Energy utilization

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Using artificial plant optimization algorithm with dynamic population and cluster methods to optimize the performance of DV-Hop

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Sensor Letters 2014年第8期12卷 1313-1317页

作者： Wu, Xuexue Cui, Zhihua Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Electric Engineering Southwest Jiaotong University Sichuan China

Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incorporating with dynamic population size and cluster methods, furthermore, to investigate the performance, APOA-DC is applied to optimize the DV-Hop localization algorithm, simulation results show it achieves better performance than the DV-Hop algorithm.

关键词： Evolutionary algorithms

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Detection on Inconsistency of Verb Phrase in TreeBank 3

Detection on Inconsistency of Verb Phrase in TreeBank

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3rd CIPS-SIGHAN Joint Conference on Chinese Language processing, CLP 2014

作者： Duan, Chaoqun Zheng, Dequan Zhu, Conghui Li, Sheng Tan, Hongye MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin150001 China Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China

Annotating linguistic data is often a complex, time consuming and expensive endeavor. Even with strict annotation guidelines, human subjects often deviate in their analyses, each bring different biases, interpretations of the task and levels of consistency. The aim of this paper is to explore a way to find out the inconsistencies in the corpus TreeBank which is used for syntactic analysis through the procedure we study the inconsistencies of verb phrase tagging in the corpus Tree- Bank. At the same time, we can analyze the inconsistencies of verb phrase tagging which are found in the corpus TreeBank in order that we can find a way to improve the consistency of verb phrase tagging automatically which is effective to improve the quality of corpus. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint Conference on Chinese Language processing. All rights reserved.

关键词： Syntactics

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Tensor-based Gaussian processes regression using a probabilistic kernel with information divergence

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International Journal of Simulation: Systems, Science and Technology 2015年第5期16卷 13.1-13.7页

作者： Tang, Lingbing Peng, Pin Zhao, Qibin Gui, Lihua Qing, Luochang Mobile E-business Collaborative Innovation Center of Hunan Province Hunan University of Commerce ChangshaHunan410205 China Key Laboratory of Hunan Province for Mobile Business Intelligence ChangshaHunan410205 China Lab.for Advanced Brain Signal Processing Brain Science Institute RIKEN 2-1 Hirosawa WakoshiSaitama Japan Graduate School of Engineering Saitama Institute of Technology Japan School of Finance and Banking Hunan University of Commerce ChangshaHunan410205 China

We present a Gaussian processes regression for tensor-valued inputs, which is based on a coherent treatment of a prior for the latent function with a covariance function defined on a tensor representation of multivariate data. Such a rigorous account of the underlying data dependencies within the model is also the basis for a new and powerful framework for structural data analysis. This is achieved under the assumption of generative models, and by introducing a family of probabilistic kernels with tensor arguments. By equipping these product kernels with the Jensen-Shannon divergence measure for tensorial data, the proposed model can be considered as both a generative and discriminative model. Further, a fully Bayesian treatment is used to estimate the hyperparameters and infer the predictive distributions. Experimental results on a real world application of decoding 3D movement trajectory based on electrocorticography (ECoG) signals recorded from a monkey brain demonstrate the effectiveness and performance advantage of the proposed framework. © 2015, UK Simulation Society. All rights reserved.

关键词： Tensors

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Fuzzy Sentiment Membership Determining for Sentiment Classification

Fuzzy Sentiment Membership Determining for Sentiment Classif...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Chuanjun Zhao Suge Wang Deyu Li School of Computer and Information Technology Key Laboratory of Computational Intelligence Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China

ISBN: (纸本)9781479942732

Traditional support vector machine treats all samples using the same weight. Therefore it is very sensitive to noisy data. While the fuzzy support vector machine assigns lower weights to the samples which make small contributions to classification, thus it is beneficial to reduce the effects of noisy and unimportant data on the classification accuracy rate. In this paper, we propose a novel fuzzy sentiment membership determining method for solving sentiment classification task. We assume that strong intensity texts make more contributions to sentiment classification, while weak intensity texts are unimportant for the classification. In order to get the fuzzy sentiment membership of review texts, this paper proposes a three-layer sentiment propagation model. Firstly, we calculate the sentiment score of texts by the interrelations of the texts, topics and words, and ensure that the absolute value of sentiment score as the fuzzy sentiment membership degree of texts. Then, we train a fuzzy support vector machine to classify the samples from the test data sets. Finally, we conduct some experiments on four English reviews data sets from Amazon shopping websites. The experimental results show that the proposed method can improve the accuracy of sentiment classification effectively.

关键词： Support vector machines Noise measurement Accuracy Data mining Training Semantics Analytical models

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A concept-drifting detection algorithm for categorical evolving data

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17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013

作者： Cao, Fuyuan Huang, Joshua Zhexue Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Key Laboratory of Computational Intelligence and Chinese Information Processing School of Computer and Information Technology Shanxi University Taiyuan 030006 China

ISBN: (纸本)9783642374555

In data streams analysis, detecting concept-drifting is a very important problem for real-time decision making. In this paper, we propose a new method for detecting concept drifts by measuring the difference of distributions between two concepts. The difference is defined by approximation accuracy of rough set theory, which can also be used to measure the change speed of concepts. We propose a concept-drifting detection algorithm and analyze its complexity. The experimental results on a real data set with a half million records have shown that the proposed algorithm is not only effective in discovering the changes of concepts but also efficient in processing large data sets. © Springer-Verlag 2013.

关键词： Rough set theory

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Closed-Label Concept Lattice Based Rule Extraction Approach

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7th International Conference on Intelligent Computing (ICIC)

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

ISBN: (纸本)9783642245527

Concept lattice is an effective tool for data analysis and extracting classification rules. However, the classical concept lattice often produce a lot of redundant rules. Closed-label concept lattice realizes reduction of concept intention, which can be used to extract fewer rules than the classical concept lattice. This paper presents a method for classification rules extraction based on the closed-label concept lattice. Examples show that the proposed method is effective for extracting more concise classification rules.

关键词： Concept lattice Closed-label concept lattice Classification rules ID3

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Hierarchical Structures on Multigranulation Spaces

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Journal of Computer Science & Technology 2012年第6期27卷 1169-1183页

作者：杨习贝钱宇华杨静宇 School of Computer Science and Engineering Jiangsu University of Science and Technology School of Computer Science and Technology Nanjing University of Science and Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University

Though many hierarchical structures have been proposed to analyze the finer or coarser relationships between two granulation spaces, these structures can only be used to compare the single granulation spaces. However, it should be noticed that the concept of multigranulation plays a fundamental role in the development of granular computing. Therefore, the comparison between two multigranulation spaces has become a necessity. To solve such problem, two types of the multigranulation spaces are considered： one is the partition-based multigranulation space, the other is the covering-based multigranulation space. Three different hierarchical structures are then proposed on such two multigranulation spaces, respectively. Not only the properties about these hierarchical structures are discussed, but also the relationships between these hierarchical structures and the multigranulation rough sets are deeply investigated. It is shown that the first hierarchical structure is consistent with the monotonic varieties of optimistic multigranulation rough set, and the second hierarchical structure is consistent to the monotonic varieties of pessimistic multigranulation rough set, the third hierarchical structure is consistent to the monotonic varieties of both optimistic and pessimistic multigranulation rough sets.

关键词： hierarchical structure multicovering rough set multigranulation rough set multigranulation space

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Sentiment clustering of product object based on feature reduction

Sentiment clustering of product object based on feature redu...

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Wang, Suge Yin, Xueqian Li, Ru Zhang, Jie Lv, Yunyun School of Computer and Information Technology Shanxi University Taiyuan 030006 China School of Mathematics Science Shanxi University Taiyuan 030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan 030006 China

ISBN: (纸本)9781467300223

Face to car domain, the content of reviews is very scattered. In order to evaluate rate of each product, we extract the sentences from reviews. Then, those sentences are merged which describe the same product together and summarizing them according to product performances. On this basis, we extract features from these sentences, and calculate their value. Owing to existing missing feature values, established information systems are called incomplete information systems. For the problems of high dimension and missing data, we adopt the feature reduction algorithm based on discernibility matrix to reduce the feature dimension. Lastly, we aggregate each product by K-means clustering algorithm. Our experimental results indicate that the proposed method is effective. © 2012 IEEE.

关键词： Clustering algorithms

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