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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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Robust smooth one-class support vector machine

Robust smooth one-class support vector machine

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International Conference on Information Technology and Electronic Commerce

作者： Jin-Kou Hu Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Hebei Province China

ISBN: (纸本)9781479953004

In this paper, a novel one-class classification approach, namely, robust smooth one-class support vector machine (RSOCSVM) is proposed. The proposed method can efficiently enhance the anti-noise ability of the traditional one-class support vector machine (OCSVM). Utilizing the smooth technique, RSOCSVM reformulates the quadratic programming problem of OCSVM as an unstrained optimization format. Moreover, half-quadratic minimization is used to solve the obtained unstrained optimization problem. Experimental results on two synthetic data sets and nine benchmark data sets demonstrate that the proposed method is superior to the traditional OCSVM.

关键词： Optimization Support vector machines Robustness Accuracy Minimization Training Benchmark testing

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Fast processing of foreign fiber images by image blocking

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Information Processing in Agriculture 2014年第1期1卷 2-13页

作者： Yutao Wu Daoliang Li Zhenbo Li Wenzhu Yang Beijing Engineering&Technology Research Center for Internet of Things in Agriculture China-EU Center for Information and Communication Technologies in Agriculture of China Agriculture UniversityBeijing100083PR China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei UniversityBaoding071002PR China

In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton *** the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber *** paper presents a new approach for the fast processing of foreign fiber *** approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image *** first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several ***,the subsequent step is to judge which image block contains the target foreign fiber image through image *** we segment the image block via OSTU which possibly contains target images after background eradication and image ***,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target *** experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation *** the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.

关键词： Cotton Foreign fibers Fast image processing Image block Image pre-decision Image connection

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Updating Approximations of VPRS Model based on Dominance Relations

Updating Approximations of VPRS Model based on Dominance Rel...

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作者： Jiajia HOU Yan LI Yan ZHAN Liang WANG Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei University

In real world problems, the collected data vary from time to time, and therefore, the approximations of a concept by a variable precision rough set model(VPRS) should be correspondingly updated. This paper focuses on developing incremental method to update set approximations of VPRS based on dominance relations. Under dynamic environments where an object is inserted or deleted, we present the updating principles and then develop the incremental method for updating approximation sets. The related theoretical results are presented with proofs, and illustrative examples are also given to support the effectiveness of the proposed method.

关键词： Variable Precision Rough Set Dominance Relation Set Approximation Dynamic Information Systems Incremental Update

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Incrementally Updating Method in Dominance- Based Rough Set Approach

Incrementally Updating Method in Dominance- Based Rough Set ...

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作者： Yan LI Xiaoqing LIU Jiajia HOU Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Dominance-based rough set approach(DRSA) can handle the attributes with preference orders, and therefore it has been widely applied in multi-criteria decision making problems. In real applications, the collected information is updated from time to time which results in dynamic information systems, especially when the attributes or objects are inserted or deleted. The traditional DRSA needs to update the set approximations whenever the information systems change, which decreases the method efficiency greatly. For classification problems with multiple criteria, this paper presents incremental algorithms to update set approximations when an object is inserted or deleted, which is expected to be more efficient than computing the approximations from the scratch. The related theoretical results are presented with proofs, and illustrative examples are also given to support the effectiveness of the proposed incremental method.

关键词： Rough Set Dominance Relation Set Approximation Dynamic Information Systems Incremental Update

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A Core Set Weighted Support Vector machines

A Core Set Weighted Support Vector Machines

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作者： Shuxia LU Limin LI Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Classification for large datasets is a classical problem in machine learning. In this paper, we focus on effevtive classification algorithm for large datasets and imbalanced datasets. First, to deal with imbalanced dataset, we define the weight according to the size of positive and negative dataset. Then, a fast learning algorithm on large datasets called a core set weighted support vector machines(CSWSVM) is proposed. In the proposed approach, the corresponding core set(CS) can be solved by employing the core vector machine(CVM) or generalized CVM(GCVM), and then the weighted support vector machines(WSVM) can be used to implement classification for imbalanced datasets. Experimental results on UCI and USPS datasets demonstrate that the proposed method is effective.

关键词： core vector machine weight support vector machine core set

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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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Efficient image representation for object recognition via pivots selection

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Frontiers of Computer Science 2014年

作者： Xie, Bojun Liu, Yi Zhang, Hui Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well-known pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to "grow-up" features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive *** this paper, we present efficient kernel descriptor (EKD) and efficient hierarchical kernel descriptor (EHKD), which are built upon incomplete Cholesky decomposition. EKD automatically selects a small number of pivot features for generating patch-level features to achieve better computational efficiency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

关键词： Object recognition

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An improved differential evolution and its application to determining feature weights in similarity based clustering

An improved differential evolution and its application to de...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Ru Dong Daniel S. Yeung Xi-Zhao Wang Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding China Machine Learning and Cybernetics Research Center South China University of Technology Guangzhou China

ISBN: (纸本)9781479902590

Feature weighting, which is considered as an extension of feature selection techniques, has been successfully applied to improve the performance of clustering. Focusing on the clustering based on a similarity matrix, we design an optimization model to minimize the fuzziness of similarity matrix by learning feature weights. The objective of this model is to get a more reasonable result of clustering through minimizing the uncertainty (fuzziness and non-specificity) of similarity matrix. To solving this optimization model effectively, we propose a new searching approach which integrates together multiple evolution strategies of both differential evolution and dynamic differential evolution. The experimental results on several benchmark datasets show that the performance of the proposed method is significantly improved compared to that of gradient-descent-based approach in terms of five selected clustering evaluation indices, i.e., fuzziness of similarity matrix, intra-class similarity, inter-class similarity, ratio of intra-class similarity to inter-class similarity.

关键词： Abstracts Noise measurement

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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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