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Hybrid discrimination method for samples classification in medicine

International Journal of Simulation: Systems, Science and technology 2016年第27期17卷 17.1-17.9页

作者： Chen, Xiaoyu Liu, Bo Xia, Xin Yan, Dandan Yan, Wang Ma, Lizhuang Department of Information Center East Hospital Tongji University School of Medicine Jimo Road 150 Shanghai China School of Continuing Education Shanghai Jiao Tong University Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Department of Computer Science Shanghai Jiao Tong University Shanghai200240 China

As indispensable solutions in classification problems, discrimination for samples has been employed in medicine, and it is performed subjectively by physicians at present, which hinders the diagnosis and treatment in medicine. In this paper, a hybrid discrimination method (HDM) in medicine is proposed, which consists of two phases, including attribute selection phase and discriminant phase. In attribute selection phase, critical attributes are selected from the original features by linear correlation and C5.0 decision tree. In discriminant phase, samples are discriminated by discriminant analysis. This discrimination method is evaluated through five datasets of chronic hepatitis B, cardiac Single Proton Emission Computed Tomography (SPECT) images, Lung Cancer, Hepatitis survival and Iris plant for demonstrating its viability and applications. Finally, this proposed method has obtained the critical clinical lab indicators and discriminants related to three syndromes in CHB dataset, and it also performs well than some typical classification methods in the other four datasets for its broader applications. © 2016, UK Simulation Society. All rights reserved.

关键词： Discriminant analysis

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Neighborhood collaborative classifiers

Neighborhood collaborative classifiers

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Suping Xu Xibei Yang Eric C. C. Tsang Eric A. Mantey Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Economics and Management Nanjing University of Science and Technology Nanjing China Faculty of Information Technology Macau University of Science and Technology Macau China

ISBN: (纸本)9781509003914

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC; 2) NCC reduces the size of dictionary when compared with CRC.

关键词： Collaboration Rough sets Training Cybernetics Numerical models Error analysis Measurement

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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An improved image saliency detection approach based on features of space 4th

An improved image saliency detection approach based on featu...

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4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Wang, Yan Yu, Weiyu Qian, Shaohui Tang, Rundong School of Electronic and Information Engineering South China University of Technology Guangzhou China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781138028302

Automatic detection salient region of images is widely used in image applications such as image editor, segmentation, retrieval and object recognition. The AC algorithm is one of the typical algorithms, which uses regional contrast to estimate salience map. It only takes into account the regional information without considering spatial information, so there are some defects in some cases. An improved algorithm is proposed, which increasingly evaluates space information by extracting depth message of the depth map. To confirm the algorithm’s effectiveness, the typical algorithms are compared with the new one. And the experimental results show that the proposed method generates high quality results. © 2015 Taylor & Francis Group, London.

关键词： Object recognition

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Predicting helix boundaries of α-helix transmembrane protein with feedback conditional random fields 11th

Predicting helix boundaries of α-helix transmembrane protei...

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11th International Conference on Intelligent Computing, ICIC 2015

作者： Wang, Kun Wu, Hongjie Lu, Weizhong Fu, Baochuan Lü, Qiang Huang, Xu School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China School of Computer Science and Technology Soochow University Suzhou215006 China Jiangsu Provincial Key Lab for Information Processing Technologies Suzhou215006 China School of Information Engineering Huzhou University Huzhou313000 China

ISBN: (纸本)9783319221793

Transmembrane proteins play an important role in cellular energy production, signal transmission, metabolism. Existing machine learning methods are difficult to model the global correlation of the membrane protein sequence, and they also can not improve the quality of the model from sophisticated sequence features. To address these problems, in this paper we proposed a novel method by a feedback conditional random fields (FCRF) to predict helix boundaries of α-helix transmembrane protein. A feedback mechanism was introduced into multi-level conditional random fields. The results of lower level model were used to calculate new feedback features to enhance the ability of basic conditional random fields. One wide-used dataset DB1 was used to validate the performance of the method. The method achieved 95% on helix location accuracy. Compared with the other predictors, FCRF ranks first on the accuracy of helix location. © Springer International Publishing Switzerland 2015.

关键词： Learning systems

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Autonomous migration of virtual machine based on OpenStack

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International Journal of Hybrid information technology 2016年第8期9卷 65-84页

作者： Zhou, Li Yu, Zhuoer Zhang, Jilin Wan, Jian Wu, Yusen School of Computer and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou310058 China School of Information and Electronic engineering Zhejiang University of Science & Technology Hangzhou310023 China Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis HangzhouZhejiang China

For the problem of compute nodes load unbalance leads to the overload node impact VM performance and the underload node waste electrical energy. Dynamic VM consolidation is an efficient approach for improving the utilization of servers and reducing energy costs. In this paper, we introduce dynamic consolidation into OpenStack and implement AMVM based on OpenStack. AMVM collect and analyze all performance data of VM and compute node in OpenStack. Then AMVM automatically schedules VM live migrate to the most appropriate node according to the real-time load of all compute nodes. Then we use hybrid-copy instead of pre-copy in AMVM to improve the efficiency of VM live migration. Finally, we analyze the performance of hybrid-copy, hybrid-copy can reduce page faults compared with post-copy and reduce total data transferred compared with pre-copy. © 2016 SERSC.

关键词： Virtual machine

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A Parallel Algorithm to Construct BISTs on Parity Cubes

A Parallel Algorithm to Construct BISTs on Parity Cubes

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International Conference on information Science and Control Engineering (ICISCE)

作者： Yan Wang Jianxi Fan Wenjun Liu Yuejuan Han Provincial Key Laboratory for Computer Information Processing Technology Soochow University School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9781467368513

Independent spanning trees(ISTs) have applications in interconnection networks such as reliable communication protocols, and secure message distribution, etc. In this paper, we use BIST to denote the IST isomorphic to binomial-like tree. We propose an algorithm to construct n BISTs and the algorithm can be parallelized on PQ n to run in O(N) time, where N = 2 n .

关键词： Broadcasting Hypercubes computer science Reliability computer network reliability Built-in self-test

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Sparse Autoencoder Based Deep Neural Network for Voxelwise Detection of Cerebral Microbleed

Sparse Autoencoder Based Deep Neural Network for Voxelwise D...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yu-Dong Zhang Xiao-Xia Hou Yi-Ding Lv Hong Chen Yin Zhang Shui-Hua Wang Hunan Provincial Key Laboratory of Network Investigational Technology Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Neurology First Affiliated Hospital of Nanjing Medical University Nanjing China Department of Psychiatry Nanjing Medical University Nanjing Jiangsu China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan Hubei China Department of Electrical Engineering The City College of New York New York NY USA

ISBN: (纸本)9781509053827

In order to detect cerebral microbleed more efficiently, we developed a novel computer-aided detection method based on susceptibility-weighted imaging. We enrolled five CADASIL patients and five healthy controls. We used a 20x20 neighboring window to generate samples on each slice of the volumetric brain images. The sparse autoencoder (SAE) was used to unsupervised feature learning. Then, a deep neural network was established using the learned features. The results over 10x10-fold cross validation showed our method yielded a sensitivity of 93.20±1.37%, a specificity of 93.25±1.38%, and an accuracy of 93.22±1.37%. Our result is better than Roy's method, which was proposed in 2015.

关键词： Sensitivity Biological neural networks Biomedical imaging Standards Neural networks Neurology Brain models Image processing Patient monitoring Blood vessels

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Multiple SVM-RFE for Multi-class Gene Selection on DNA Microarray Data

Multiple SVM-RFE for Multi-class Gene Selection on DNA Micro...

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International Joint Conference on Neural Networks

作者： Li Zhang Xiaojuan Huang School of Computer Science and Technology & Collaborative Innovation Center of Novel Software Technology and Industrialization Soochow University Suzhou 215006 Jiangsu China School of Computer Science and Technology & Provincial Key laboratory for Computer Information Processing Soochow University Suzhou 215006 Jiangsu China

ISBN: (纸本)9781479919611

This paper presents a new multi-class gene selection and classification method based on multiple support vector machine recursive feature elimination (SVM-RFE). For a multi-class DNA microarray problem, we solve it as multiple binary classification problems. First, the one-versus-all method is used to decompose the multi-class task into multiple binary problems. Second, an SVM-RFE is adopted to select genes for each binary problem. Then, an SVM classifier is used to train the selected gene data for a binary problem. Finally, we combine the outputs of multiple SVM classifiers. Experimental results on three DNA Microarray datasets show that the proposed method achieves higher classification accuracy.

关键词： support vector machine feature selection recursive feature elimination multi-class classification DNA microarray data Support Vector Network Feature extraction multi-class DNA Microarrays Phyla Genes Binary Classification Methods

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