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Highly imbalanced classification using improved rotation forests

International Journal of Wireless and Mobile Computing 2016年第1期10卷 35-41页

作者： Fang, Xiaonan Zheng, Xiyuan Tan, Yanyan Zhang, Huaxiang Department of Information Science and Engineering Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University Jinan China

Imbalanced data classification is a challenging problem in data mining. It happens in many real-world applications and has attracted growing attentions from researchers. This issue occurs when the number of one class is much higher than the other class. Ensemble of classifiers has been well known as an effective solution. Then, two novel ensemble algorithms (RUROForest and SROForest) based on rotation forests are proposed for solving highly imbalanced problems. Random undersampling or SMOTE approaches are combined with rotation forest in the proposed algorithms, which balance the uneven distribution of data sets and keep the diversity of single classifier as well. Focused on two-class highly imbalanced problems, 22 relevant data sets are performed in experiments. Experimental results and statistical analyses show that our proposed methods overtake the state-of-theart ensemble methods on the most widely used imbalanced measure criterion AUC. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： Data mining

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Cloud Data Life Cycle Security Issues and Research of Encryption Technology

Cloud Data Life Cycle Security Issues and Research of Encryp...

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2016 International Conference on Communications, Information Management and Network Security (CIMNS2016)

作者： Nengneng Li Yongsheng Zhang Yueqin Fan Liang Chang School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology

ISBN: (纸本)9781510830981

With the further development of the cloud computing, the security of the cloud computing is becoming increasingly prominent. Based on analyzing the development status of cloud computing, this article will focus on studying the security of cloud computing in data, and discuss the data security problems in the process of data life cycle in cloud environment. Then the paper will put forward the corresponding measures and data encryption technology, in order to protect the data security of cloud computing.

关键词： cloud computing cloud security data security encryption erase technology

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A sample weighted sparse representation approach to image classification

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ICIC Express Letters, Part B: Applications 2016年第11期7卷 2283-2290页

作者： Wang, Yongxin Zhang, Huaxiang Department of Computer Science Shandong Normal University China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology No. 88 Wenhua East Road Lixia District Jinan250014 China

Sparse representation for classification (SRC) has achieved a big success for face recognition. It utilizes a sparsely linear combination of the training samples to construct a test sample, and classifies the test sample based on the reconstruction error associated with each class. Since SRC weights each training sample equally important, which may not hold for real applications, we propose a novel sample weighted sparse representation classification approach (SWSRC) by weighting each training sample differently. We first employ the representation ability of each training sample to construct a weight matrix and then solve a weighted l1minimization problem to obtain the sparse reconstruction coefficients. Experimental results on AR, YaleB and USPS image datasets demonstrate its effectiveness. © 2016 ICIC International.

关键词： Face recognition

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A clustering algorithm based on maximum entropy principle

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Journal of Physics: Conference Series 2017年第1期887卷 012064-012064页

作者： Zhao, Yang Liu, Fangai College of Information Science and Engineering Shandong Normal University Jinan Shandong 250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan Shandong 250358 China

Aiming at the shortcomings of clustering performance of many traditional text clustering methods, a clustering algorithm based on maximum entropy principle is proposed. The algorithm uses the cosine similarity measure cited in the traditional text clustering algorithm SP-Kmeans, and then introduces the maximal entropy theory to construct the maximal entropy objective function suitable for text clustering. The maximum entropy principle is introduced into the spherical K-mean text clustering Algorithm. The experimental results show that compared with DA-VMFS and SP-Kmeans algorithms, in addressing the large number of text clustering problem. The performance of CAMEP clustering algorithm is greatly improved, and has a good overall performance.

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Improved artificial bee colony algorithm and application in path planning of crowd animation

International Journal of Control and Automation

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International Journal of Control and Automation 2015年第3期8卷 53-66页

作者： Jing, Bai Hong, Liu School of Information Science and Engineering College Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

Crowd animation is a new and continuous challenge in computer animation. In tradition, crowd animation can be realized by key frame technology, and animators should set every character's expression, action, motion, and behavior. Therefore animators' workload by hand will increase tremendously with characters growing, which lead to difficult to realize crowd animation for low efficiency and poor global controllability, especially in path planning by appointing a target position for each individuals. In order to overcome these, an improved artificial bee colony (IM-ABC) algorithm is proposed to apply on the path planning of crowd animation. The IM-ABC is fit to simulate the crowd motion in animation based on the following two merits over the others in crowd animation. One is the rule of role transformation, which can make the rapid convergence of the result and avoid getting trapped in the local optima. The other is the realization of multi-object optimization in the process of iteration, which reaches the uniformly distributed result of swarm motion and especially fits to realize the path plan. In this paper, we simply reviews classical ABC algorithm proposed by Karaboga at the beginning. Then, in order to speed the convergence and make individuals generate paths more realistic and natural, some measures are taken to modify the classical ABC (called IM-ABC) algorithm, which include initializing colony based on chaos sequence, self-adaptively selecting the follower bees, and adaptively controlling parameters, etc. After the experiments of benchmark functions, the results confirm that the IM-ABC have better performance than the classical ABC algorithm and others. Finally, the IM-ABC algorithm is used for path planning to generate the route from the initial to the destination without collision. Through simulation experiments based on four motion models it is showed that this method can succeed generating the optimum paths with efficiency, intelligence, and natural featu

关键词： Motion planning

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Community Discovery Algorithm Based on Potential Energy in Complex Network 34

Community Discovery Algorithm Based on Potential Energy in C...

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第三十四届中国控制会议

作者： LIU Shuangshuang WANG Hong School of Information Science and Engineering Shandong Normal University Distributed in Shandong province key laboratory of computer software

ISBN: (纸本)9781467374439

In recent years,our understanding of complex networks has *** structure as a common characteristic of complex networks has become an important direction in the study of complex ***,people put forward many community detection *** original Largest Fitness Measure algorithm,the selection of seed node is random,community division needs to be improved,and it is difficult to achieve its end *** on above problems,we propose a kind of Weight Largest Fitness Measure *** to the thought of potential energy,the new algorithm optimizes and handles initial node,simplify node fitness function and expand community according to potential ***,through two groups of experimental validate the performance of the *** experimental results show that,compared with Largest Fitness Measure algorithm,the new algorithm has higher accuracy and shorter run time.

关键词： Complex Network,Community Detection,Potential Energy

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A Clustering Method of Chinese Medicine Prescriptions Based on Modified Firefly Algorithm

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Chinese Journal of Integrative Medicine 2016年第12期22卷 941-946页

作者：袁锋刘弘陈守强徐亮 School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University School of Information Engineering Shandong Management University Heart Center Second Hospital Affiliated to Shandong University of Traditional Chinese Medicine School of Traditional Chinese Medicine Shandong University of Traditional Chinese Medicine

This paper is aimed to study the clustering method for Chinese medicine（CM） medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.

关键词： clustering firefly algorithm simulated annealing algorithm Chinese medicine prescriptions

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An improved mixed-type data based kernel clustering algorithm

An improved mixed-type data based kernel clustering algorith...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Min Ren Peiyu Liu Zhihao Wang Xiao Pan School of Mathematic and Quantitative Economics Shandong University of Finance and Economics Jinan Shandong province China School of Information Science and Engineering Shandong Normal University Jinan Shandong province China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan Shandong province China

ISBN: (纸本)9781509040940

Clustering algorithm is often used to analyze the communication data for network intrusion detection system. However, network communication data are mixed, e.g., numerical and categorical data. So, at first, this paper put forward a method for representing the cluster center (prototype) of mixed-type data. Then respectively in combination with the continuity characteristic of the numerical attributes and the semantic feature of the categorical attributes, the dissimilarity measurement formula was improved by use of the Gauss kernel function, on the base of which, defined the objective function. After that this paper further put forward an Improved Mixed-type Data based Kernel Clustering Algorithm (IKCA-MD), which showed a stable clustering result because the initial cluster centers are obtained by Maximum Density and Distance method (MDD). Finally the feasibility and effectiveness of the method for the network intrusion detection were verified by experiments.

关键词： Clustering algorithms Kernel Prototypes Intrusion detection Linear programming Training Testing

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Microblog Orientation Analysis Based on Connnectives 10

Microblog Orientation Analysis Based on Connnectives

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10th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2015

作者： Zhao, Jun Wang, Hong School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China

ISBN: (纸本)9781467383158

At present, a larger number of researchers analyzed Micro-blog orientation and they concentrated their energy on emotional words, adverb and negative words without considering the impact of other emotional factors, such as the coordinative relation, progressive relation and adversative relation. So a method studying the sentiment polarity of associated words was proposed. The structure characteristics of associated words were analyzed sufficiently, and a turning words dictionary, a Chinese emotion dictionary and a negative words dictionary were founded based on the existing dictionary. This paper considers the combination laws of negative words, adversative words and conjunctions into Micro-blog. In the experiments, we verify this method can analyze the sentiment polarity of transition sentences efficiently and also has higher classification accuracy. © 2015 IEEE.

关键词： Chinese Microblog Connectives Emotional Dictionary Orientation Analysis SVM

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A Community Evolution Model Based on Visibility Graph

A Community Evolution Model Based on Visibility Graph

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第三十四届中国控制会议

作者： WANG Hong SUN Yan-shen School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Department of Earth Sciences Zhejiang University

ISBN: (纸本)9781467374439

One of the most important work to analyze networks is community detection. We present a dynamic community discovery method based on Visibility Graph. Firstly, we put forward related definitions of Visibility Graph for multi-dimensional time series. Then, we present algorithms to describe how to use Visibility Graph in finding communities in complex networks. Finally, we present algorithms to find dynamic multi-relational communities. We apply our method in some real data sets. Experimental results show that our methods do better for finding dynamic communities in complex networks.

关键词： community evolution,visibility graph,community discovery,complex network

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