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International Conference on machine learning and Cybernetics (ICMLC)

作者： Hao Chen Yan Zhan Yan Li Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

Email is a kind of semi-structured document, some important attributes are contained in its structure, and especially using spam-specific features could improve the email classification results. In this paper, we apply decision tree data mining technique to dig out the potential association rules among these attributes of email, and then to identify unknown email's category based on these rules. According to the experiment of applying numerous Chinese emails to our email classifier, the efficiency of our method is not lower than that of other existing methods of checking whole email content text. Meanwhile our method can reduce the cost of computation and consumption of system resources.

关键词： Electronic mail Postal services Classification algorithms machine learning Classification tree analysis Association rules

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Rotation Based Ensemble of One-Class Support Vector machines

Rotation Based Ensemble of One-Class Support Vector Machines

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Wei-Tao Liu Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9781538652152

One-class support vector machine (OCSVM) is regarded as an important one-class classification method for tackling the problem of extreme class imbalance. However, combining several OCSVMs by the traditional ensemble approaches may not improve the performance of the single OCSVM because it is known as a strong classifier. In the paper, a rotation based ensemble method is proposed for integrating OCSVMs., The training data of different OCSVMs in the ensemble is transformed by different rotation matrices. Therefore, the diversity of training data for the ensemble of OCSVMs can be guaranteed. Experiments conducted on ten benchmark data sets to validate the effectiveness of the proposed ensemble strategy.

关键词： Training Support vector machines Training data Bagging Principal component analysis Forestry Diversity reception

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KM-A∗ pathfinding algorithm based on hierarchical clustering and strengthened DB Index criteria

KM-A∗ pathfinding algorithm based on hierarchical clusterin...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Cai Chen Yan Li Tie-Song Li Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

In computer games, high-quality pathfinding algorithms are important to bring satisfactory experiences to the players, which may improve the playability of computer game. The method of KM-A* belongs to hierarchical pathfinding, which incorporates the information of obstacle distribution when partitioning game maps using K-means clustering. This algorithm reduced unnecessary storage and unnatural paths in some open areas. However, the found path by KM-A* is very unstable and the quality closely depends on the clustering results. In this paper, we proposed an improved KM-A* algorithm, which aims to enhance the stability and quality of pathfinding. First, an agglomerative hierarchical clustering algorithm is used to determine the initial cluster centers, and K-Means algorithm is then applied to cluster obstacle nodes with k values which can possibly generate best cluster categories on game maps. Secondly, the strengthened form of DB Index criteria is defined by adding impact factors to the standard DB Index criteria to measure the quality of clustering results. Finally the best clustering is selected based on this new DB Index, which will provide a guidance to further generate an abstract map for on-line pathfinding. Experiments are conducted to compare the performance of KM-A* and improved KM-A*, which show that the latter can find more stable paths and use less on-line processing time.

关键词： Indexes Clustering algorithms Games Partitioning algorithms machine learning Algorithm design and analysis Heuristic algorithms

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Support vector machine based on a new reduced samples method

Support vector machine based on a new reduced samples method

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Shu-Xia Lu Jie Meng Gui-En Cao Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

The support vectors play an important role in the training to find the optimal hyper-plane. For the problem of many non-support vectors and a few support vectors in the classification of SVM, a method to reduce the samples that may be not support vectors is proposed in this paper. First, adopt the Support Vector Domain Description to find the smallest sphere containing the most data points, and then remove the objects outside the sphere. Second, remove the edge points based on the distance of each pattern to the centers of other classes. In comparison with the standard SVM, the experimental results show that the new algorithm in the paper is capable of reducing the number of samples as well as the training time while maintaining high accuracy.

关键词： Training Accuracy Classification algorithms Kernel Support vector machine classification machine learning

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A survey on active learning strategy

A survey on active learning strategy

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Li-Li Sun Xi-Zhao Wang Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

Active learning is a hot topic in machine learning field. The main task of active learning is to automatically select the representative instances for efficiently reducing the sample complexity. This paper presents a brief survey of active learning regarding selection methods, query strategies, applications and other related works.

关键词： machine learning Training Classification algorithms Uncertainty learning systems Complexity theory Support vector machines

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Deep Web Entity Identification Method Based on Improved Jaccard Coefficients

Deep Web Entity Identification Method Based on Improved Jacc...

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International Conference on Research Challenges in Computer Science (ICRCCS)

作者： Yu Wang Ying-hua Li Key Laboratory in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

There are a large number of accessible deep Web sites on the Internet. However, even if identical entity has different representation formats on different Web sites. So entity identification plays a crucial role in deep Web data mining. This paper proposes an entity identification method in the field of Chinese books. First, using improved Jaccard coefficients to calculate similarity of text attributes. Second, AHP (analytic hierarchy process) is used to obtain the weights, and using the sum of weights to calculate the entity similarity. Finally, it needs to integrate duplicate entity to achieve the entity identification. The experiment results demonstrate the approach has higher accuracy with good feasibility.

关键词： Internet Data mining Books Computer science Support vector machines Web pages machine learning computational intelligence learning systems Competitive intelligence

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An Improved Analysis Approach of Overbrace/Underbrace Structure in Printed Mathematical Expressions

An Improved Analysis Approach of Overbrace/Underbrace Struct...

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Asia-Pacific Conf on (CICC-ITOE) Innovative Computing and Communication, Intl Conf on and Information Technology and Ocean Engineering

作者： Zhuo-lun Li Xue-dong Tian Key Laboratory in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

In printed mathematical expression recognition, structural analysis is a key step which determines the overall recognition results. As a special structure in expressions, overbrace/underbrace structure becomes a major difficulty of structural analysis as they're difficult to be distinguished from other structures. On the basis of baseline analysis method of expression structures, this paper presents an improved approach of overbrace/underbrace structure combining region location with variable thresholds. This approach introduces overrun threshold of overbrace/underbrace symbol, which is combined with other features to establish the analysis rules and optimized by testing on a variety of overbrace/underbrace structures. The experimental results show that the approach is effective in determining and analyzing overbrace/underbrace structure.

关键词： Information analysis Underwater communication Information technology Oceans machine learning computational intelligence Educational institutions Mathematics Computer science Testing

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An improved hybrid model for order quantity allocation and supplier risk exposure

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International Journal of Fuzzy System Applications 2016年第3期5卷 120-147页

作者： Ng, Peh Sang Zhang, Feng Department of Physical and Mathematical Science Universiti Tunku Abdul Rahman Jalan Universiti Perak Malaysia Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Hebei China

This paper investigates the risk exposure arising from the supplier evaluation criteria of cost, quality, delivery, and flexibility. An integrated method of Fuzzy Decision Making Trial and Evaluation laboratory (FDEMATEL) and Fuzzy Analytical Network Process (FANP), which is able to address interaction and feedback effects between the criteria and subjectivity in the decision rating, is used to generate the importance weights of the objectives for a network relationship problem. Different weights of the objectives are then incorporated into a Fuzzy Multi-Objective Programming (FMOP) model for determining the optimal order quantity allocated to the suppliers. The proposed method provides a more realistic decision making scenario by considering different importance weights of criteria from individual priority. Therefore, it provides a practical method to solve the real world problems. The validity of the proposed method is illustrated by a case study from a trading company. © 2016, IGI Global.

关键词： Risk assessment

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Chinese Functional Chunk Identification Based on Part of Speech

Chinese Functional Chunk Identification Based on Part of Spe...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Cheng-Yan Wang Xin-Fu Li Xue-Dong Tian College of Mathematics and Computer Science Hebei University Baoding China College of Mathematics and Computer Scienc Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province Hebei University Baoding China

Chinese functional chunk describes the basic skeleton of the Chinese sentences. It is the important bridge for joining syntax and semantic description, and the Chinese functional chunk identification plays a key role in the field of natural language processing. Identifying the functional chunk is mostly based on methods of rules which come from the syntactic analysis. In this paper, the functional chunk is mainly identified according to the effect of the part of speech in the syntactic analysis. The experimental results show the proposed method works well on the functional chunk identification.

关键词： Tagging Speech Syntactics Educational institutions Statistical analysis Information processing Particle separators

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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