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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Spetsieris, Phoebe G. Dhawan, Vijay Eidelberg, David N Shore LIJ Hlth Syst Ctr Neurosci Feinstein Inst Med Res Manhasset NY 11030 USA

ISBN: (纸本)9781424441242

Abnormal physiological networks of brain areas in disease can be identified by applying specialized multivariate computational algorithms based on principal component analysis to functional image data. Here we demonstrate the reproducibility of network patterns derived using positron emission tomography (PET) data in independent populations of parkinsonian patients for a large, clinically validated data set comprised of subjects with idiopathic Parkinson's disease (iPD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Correlation of voxel values of network patterns derived for the same condition in different data sets was high. To further illustrate the validity of these networks, we performed single subject differential diagnosis of prospective test subjects to determine the most probable case based on a subject's network scores expressed for each of these distinct parkinsonian syndromes. Three-fold cross-validation was performed to determine accuracy and positive predictive rates based on networks derived in separate folds of the composite data set. A logistic regression based classification algorithm was used to train in each fold and test in the remaining two folds. Combined accuracy for each of the three folds ranged from 82% to 93% in the training set and was approximately 81% for prospective test subjects.

关键词： abnormal physiological networks brain classification algorithm diseases idiopathic Parkinson disease image classification logistic regression medical image processing multiple system atrophy network pattern voxel values neural nets neurophysiology parkinsonian brain patterns parkinsonian syndromes positron emission tomography positron emission tomography progressive supranuclear palsy regression analysis single subject differential diagnosis

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Transformer Fault Diagnosis Based on Support Vector Machine

Transformer Fault Diagnosis Based on Support Vector Machine

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3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)

作者： Zhang, Yan Zhang, Bide Yuan, Yuchun Pei, Zichun Wang, Yan Xihua Univ Inst Elect & Informat Chengdu 610039 Peoples R China

ISBN: (纸本)9781424455379

Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small sample. However, SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power transformer. In this study, support vector machine is proposed to analysis dissolved gases content in power transformer oil, among which cross-validation is used to determine free parameters of support vector machine. The experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM model. The experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small sample. Consequently, the SVM model is a proper alternative for diagnosing power transformer fault.

关键词： support vector machine classification algorithm cross-validation free parameters fault diagnosis

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A General Decision Layer Text classification Fusion Model

A General Decision Layer Text Classification Fusion Model

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2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Xiao-Dan Zhang Institute of Scientific and Technical Information of China Beijing 100038 P.R.China

An general decision layer text classification fusion model for higher precision, is proposed, which based on model theory of information fusion, and different classification algorithm of the feature layer fusion centre having different pre-processing, their classification results input into the decision layer fusion centre separately. And the final classification result output from decision layer fusion centre. KNN, SVM and BP Net are used in feature layer, and D-S Theory is used in decision layer. The model is realized in the experiment. From the experiment and contrast, the text classification fusion model can improve the classification precision effectively.

关键词： text classification decision layer classification fusion model classification algorithm information fusion

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Transformer Fault Diagnosis Based on Support Vector Machine

Transformer Fault Diagnosis Based on Support Vector Machine

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Yan Zhang,Bide Zhang,Yuchun Yuan Institute of Electrical and Information Xihua University Chengdu,China (610039) Zichun Pei,Yan Wang Institute of Electrical and Information Xihua University Chengdu,China (610039)

Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power *** of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training *** vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small ***,SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power *** this study,support vector machine is proposed to analysis dissolved gases content in power transformer oil,among which cross-validation is used to determine free parameters of support vector *** experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM *** experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small ***,the SVM model is a proper alternative for diagnosing power transformer fault.

关键词： support vector machine classification algorithm cross-validation free parameters fault diagnosis

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Improving KNN Based Text classifications

Improving KNN Based Text Classifications

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The 2010 2nd International Conference on Future Computer and Communication(第二届未来计算机与通信国际会议 ICFCC 2010)

作者： Zongli Jiang Yi Deng School of Computer Science and Engineering Beijing University of Technology 100124 Beijing China

ISBN: (纸本)9781424458219

In a variety of text classification algorithm, KNN is a competitive one with simple implementation and high efficiency. However, with the expansion of the size of the text, the runtime of KNN will grow rapidly that cannot be afford. In this paper, we improve the KNN by introducing the kd-tree storage structure and reducing the sample space through the sample clestering methods. And experiment shows that the runtime of improved KNN algorithm reduce apparently.

关键词： classification algorithm KNN clustering

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Application of Ant Colony Optimization-SVM in Fault Diagnosis for Rectifier Circuit

Application of Ant Colony Optimization-SVM in Fault Diagnosi...

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2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： Xu Binghui Taizhou Vocational & Technical College Taizhou 318000,China

Failure of rectifier circuit has the characteristics of latency and complexity,which leads to the difficulty to fault diagnosis for rectifier circuit.A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the *** experimental object is provided and the six ACO-SVM classifiers are developed to identify the following seven states of the experimental *** testing results demonstrate that the ACO-SVM classifier has higher diagnostic accuracy than normal support vector machine and BP neural network.

关键词： rectifier circuit ant colony optimization classifier fault diagnosis classification algorithm

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Study on Decision classification Fusion Model

Study on Decision Classification Fusion Model

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9th International Conference on Hybrid Intelligent Systems (HIS 2009)

作者： Zhang, Xiao-dan Niu, Zhen-dong Beijing Inst Technol Univ Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9780769537450

In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are Input into decision level, the last classification result is output. This model is applied into improving precision of text classification. And the model is used to the Computer Center of some department. Through the experiment, the text classification fusion model can improve the classification precision effectively

关键词： text classification decision layer classification fusion model classification algorithm information fusion

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classification for Trademark Image Based on Normal Cloud Model

Classification for Trademark Image Based on Normal Cloud Mod...

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2nd International Conference on Information Management, Innovation Management and Industrial Engineering

作者： Qi, Ya-Li Beijing Inst Graph Commun Dept Comp Beijing 102600 Peoples R China

ISBN: (纸本)9780769538761

classification is an important research topic in the field of image data mining. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough sets, etc. This paper proposed a method to classify the image with normal cloud model which is an uncertainty transformation model between quantities and qualities conception. We develop the algorithm for classification based on normal cloud model. Finally we perform experiments on an artificial trademark image database. The results show the advantages of the cloud model in the process of classification.

关键词： data mining thecloud model classification algorithm trademark image retrival

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SYMBOLIC ONE-CLASS LEARNING FROM IMBALANCED DATASETS: APPLICATION IN MEDICAL DIAGNOSIS

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2009年第2期18卷 273-309页

作者： Mena, Luis Gonzalez, Jesus A. Univ Zulia Fac Engn Dept Comp Sci Maracaibo 4011 Venezuela Natl Inst Astrophys Opt & Elect Dept Comp Sci Puebla Mexico

When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make an overview of the class imbalance problem;we review consequences, possible causes and existing strategies to cope with the inconveniences associated to this problem. As an effort to contribute to the solution of this problem, we propose a new rule induction algorithm named Rule Extraction for MEdical Diagnosis (REMED), as a symbolic one-class learning approach. For the evaluation of the proposed method, we use different medical diagnosis datasets taking into account quantitative metrics, comprehensibility, and reliability. We performed a comparison of REMED versus C4.5 and RIPPER combined with over-sampling and cost-sensitive strategies. This empirical analysis of the REMED algorithm showed it to be quantitatively competitive with C4.5 and RIPPER in terms of the area under the Receiver Operating Characteristic curve (AUC) and the geometric mean, but overcame them in terms of comprehensibility and reliability. Results of our experiments show that REMED generated rules systems with a larger degree of abstraction and patterns closer to well-known abnormal values associated to each considered medical dataset.

关键词： Machine learning imbalanced datasets one-class learning classification algorithm rule extraction

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Feature-based Approach to Chinese Term Relation Extraction

Feature-based Approach to Chinese Term Relation Extraction

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International Conference on Signal Processing Systems (ICSPS 2009)

作者： Sun Xia Dong Lehong NW Univ Xian Dept Comp Sci & Technol Xian 710127 Peoples R China

ISBN: (纸本)9780769536545

In this paper, we propose a feature-based Chinese term relation extraction approach that combined the advantages of both naive bayes algorithm and perceptron algorithm. A subset of the features was estimated in training data;another subset of the features was trained by discriminative function. The results demonstrate that the proposed hybrid algorithm almost always outperforms the naive bayes algorithms and perceptron algorithms whether the training set is small or not. On the other hand, a novel feature representation was proposed, which included term sequence feature, term appearance features and context information features. Comparing the previous method, long-range dependence was considered in the proposed feature representation, which add the position of feature into vector space model (VSM) and promotes the capability of feature representation. Further, punctuation feature is the important character for terms relation extraction.

关键词： term relation extraction classification algorithm feature representation

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