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11th IEEE International Conference on Data Mining, ICDM 2011

作者： Hu, Po Huang, Minlie Xu, Peng Li, Weichang Usadi, Adam K. Zhu, Xiaoyan Department of Computer Science and Technology State Key Laboratory on Intelligent Technology and Systems Tsinghua University Beijing China ExxonMobil Research and Engineering Company NJ United States

ISBN: (纸本)9780769544083

Though news readers can easily access a large number of news articles from the Internet, they can be overwhelmed by the quantity of information available, making it hard to get a concise, global picture of a news topic. In this paper we propose a novel method to address this problem. Given a set of articles for a given news topic, the proposed method models theme variation through time and identifies the breakpoints, which are time points when decisive changes occur. For each breakpoint, a brief summary is automatically constructed based on articles associated with the particular time point. Summaries are then ordered chronologically to form a timeline overview of the news topic. In this fashion, readers can easily track various news topics efficiently. We have conducted experiments on 15 popular topics in 2010. Empirical experiments show the effectiveness of our approach and its advantages over other approaches. © 2011 IEEE.

关键词： Data mining

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Heart disease classification using neural network and feature selection

Heart disease classification using neural network and featur...

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21st International Conference on systems engineering, ICSEng 2011

作者： Khemphila, Anchana Boonjing, Veera Software Systems Engineering Laboratory Department of Mathematics and Computer Science King Mongkut's Institute of Technology Ladkrabang Chalongkrung Rd. Ladkrabang Bangkok 10520 Thailand

ISBN: (纸本)9780769544953

In this study, we introduces a classification approach using Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm and a feature selection algorithm along with biomedical test values to diagnose heart disease. Clinical diagnosis is done mostly by doctor's expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis. In many cases, not all the tests contribute towards effective diagnosis of a disease. Our work is to classify the presence of heart disease with reduced number of attributes. Original, 13 attributes are involved in classify the heart disease. We use Information Gain to determine the attributes which reduces the number of attributes which is need to be taken from patients. The Artificial neural networks is used to classify the diagnosis of patients. Thirteen attributes are reduced to 8 attributes. The accuracy differs between 13 features and 8 features in training data set is 1.1% and in the validation data set is 0.82%. © 2011 IEEE.

关键词： Data mining

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Tuning of unscented Kalman filter based biogeography-based optimization for UGVs navigation

Tuning of unscented Kalman filter based biogeography-based o...

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2011 International Conference on Automation and Robotics, ICAR 2011

作者： Su, Kuifeng Deng, Zhidong Huang, Zhen Department of Computer Science State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing 100084 China Academy of Armored Force Engineering Beijing 100072 China

ISBN: (纸本)9783642255526

In the case of linear systems, corrupted by white Gaussian noise, the Kalman filter is proved to be an optimal filter in the mean least square sense. When the system model and measurements are non-linear, variation of Kalman filter like extended Kalman filter (EKF), Unscented Kalman filters (UKF) and Particle filter (PF) are used. However, the best performance of UKF is achieved when the random variables only are Gaussian, and affected seriously by the estimation precision of noise covariance. In this paper, we give a noise covariance estimation method based Biogeography-based Optimization (BBO). The experimental results obtained from real-world road tests validate the performance of our approach. © 2011 Springer-Verlag.

关键词： Extended Kalman filters

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Evaluating effectiveness of risk identification and management using organisational models

Evaluating effectiveness of risk identification and manageme...

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IEEE International Conference on computer Research and Development

作者： Bhuiyan, Moshiur Rana, Sohel Krishna, Aneesh Decision Systems Laboratory School of Computer Science and Software Engineering University of Wollongong NSW 2522 Australia OptInfra Systems Sydney NSW Australia Department of Computing Faculty of Science and Engineering Curtin University of Technology Perth WA 6102 Australia

ISBN: (纸本)9781612848372

In this paper firstly we discuss an approach for supporting risk identification with the use of higher-level organizational models. We provide some intuitive metrics for extracting measures of actor criticality and vulnerability from organizational models. This helps direct risk management to areas of critical importance within organization models. Secondly, we provide details of an experiment that we conducted to evaluate our methodology. We believe our novel approach, will provide added benefits when used with other approaches to risk management during business process management, that do not reference the greater organizational context during risk assessment. © 2011 IEEE.

关键词： Risk management

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Learning from imbalanced data sets with a Min-Max modular support vector machine

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中国电气与电子工程前沿 2011年第1期6卷 56-71页

作者： Bao-Liang LU Xiao-Lin WANG Yang YANG Hai ZHAO Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong UniversityShanghai 200240China Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Maritime UniversityShanghai 201306China

Imbalanced data sets have significantly unequal distributions between *** between-class imbalance causes conventional classification methods to favor majority classes,resulting in very low or even nO detection of minority classes.A Min-Max modular support vector machine(M3-SVM)approaches this problem by decomposing the training input sets of the majority classes into subsets of similar size and pairing them into balanced two-class classification *** approach has the merits of using general classifiers,incorporating prior knowledge into task decomposition and parallel *** on two real-world pattern classification problems,international patent classification and protein subcellar localization,demonstrate the effectiveness of the proposed approach.

关键词： imbalanced data Min-Max modular network(M3-network) prior knowledge parallel learning support vector machine (SVM)

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Analyzing interaction communication networks in enterprises and identifying hierarchies

Analyzing interaction communication networks in enterprises ...

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IEEE Network science Workshop

作者： Wang, Yi Iliofotou, Marios Faloutsos, Michalis Wu, Bin Department of Computer Science and Engineering University of California Riverside 92507 United States Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (纸本)9781457710490

With the proliferation of electronic modes of communication (e.g., e-mails, short messages), a group of people in an enterprise can form several distinct Communication Interaction Networks, or CINs for short. A CIN is essentially a graph representation of who talks to whom among a group of individuals. In this paper, we conduct an empirical study of two modern enterprises and focus on three main questions: (Q1) how CINs from the two enterprises look, (Q2) how employees use the different available communication modes within an enterprise, and (Q3) how much information we can extract regarding the roles of their participants. We address these questions using empirical CINs from the Enron Corporation and a communication provider, using information from the exchange of e-mails, phone-calls, and short messages (SMS). For Q1, we reveal the following key structural properties that are shared by all the CINs in our study: they have high edge density, high clustering coefficient, and close to zero assortativity coefficient. For Q2, we observe that employees have differences in how they use the various communication modes. This suggests that different CINs capture different behavioral properties within an enterprise. For Q3, we propose HumanRank, a method of ranking individuals based on their importance (e.g., CEOs having higher rank than ordinary employees) using only the interactions between them. Next, using HumanRank, we introduce an unsupervised and parameter-free algorithm that identifies hierarchies by separating managers from ordinary employees. Our algorithm achieves above 70% accuracy and outperforms the state-of-the-art [1]. © 2011 IEEE.

关键词： Human resource management

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Frontier-based multi-robot map exploration using particle swarm optimization

Frontier-based multi-robot map exploration using particle sw...

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IEEE Symposium on Swarm Intelligence

作者： Wang, Yiheng Liang, Alei Guan, Haibing School of Software Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai 200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai 200240 China

ISBN: (纸本)9781612840529

Exploring an unknown environment using team of autonomous mobile robots is an important task in many real-world applications. Many existing map exploration algorithms are based on frontier, which is the boundary between unexplored space and known open space. In the context of multiple robots, the main problem of frontier-based algorithm is to choose appropriate target points for the individual robots so that they can efficiently explore the different part of the common area. This paper proposed a novel distributed frontier-based map exploration algorithm using Particle Swarm Optimization model for robot coordination. In this algorithm, the robot keeps moving to the nearby frontier to reduce the size of the unknown region, and is navigated towards frontier far away based on the PSO model after exploring the local area. The exploration is completed when there are no frontier cells on the map. Our algorithm has been implemented and tested both in simulation runs and real world experiment. The result shows that our method has a good scalability and efficiency. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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An hybrid model through the fusion of sensitivity based linear learning method and type-2 fuzzy logic systems for modeling PVT properties of crude oil systems

An hybrid model through the fusion of sensitivity based line...

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2011 5th Malaysian Conference in software engineering, MySEC 2011

作者： Olatunji, Sunday Olusanya Selamat, Ali Raheem, Abdul Azeez Abdul Intelligent Software Engineering Laboratory Faculty of Computer Science and Information Systems University Teknologi Malaysia 81310 UTM Skudai Johor Malaysia Centre for Petroleum and Minerals Research Institute King Fahd University of Petroleum and Mineral Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781457715310

Sensitivity based linear learning method (SBLLM) has recently been used as predictive tool due to its unique characteristics and performance, particularly its high stability and consistency during predictions. However, the generalization capability of SBLLM is sometimes limited depending on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. In order to reduce the effects of uncertainties in SBLLM prediction and improve its generalization ability, this paper proposes a hybrid system through the unique combination of type-2 fuzzy logic systems (type-2 FLS) and SBLLM;thereafter the hybrid system was used to model PVT properties of crude oil systems. In the proposed hybrid, the type-2 FLS is used to handle uncertainties in reservoir data so that the final output from the type-2 FLS is then passed to the SBLLM for training and then final prediction using testing dataset follows. Comparative studies have been carried out to compare the performance of the proposed T2-SBLLM hybrid system with each of the constituent type-2 FLS and SBLLM. Empirical results from simulation show that the proposed T2-SBLLM hybrid model greatly improved upon the performance of SBLLM. © 2011 IEEE.

关键词： Feedforward neural networks

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Incorporating cellular sorting structure for better prediction of protein subcellular locations

Incorporating cellular sorting structure for better predicti...

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作者： Yang, Wen-Yun Lu, Bao-Liang Kwok, James T. Department of Computer Science and Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Shanghai China MOE Microsoft Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University No. 800 Dongchuan Road Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Hong Kong

This article explores the interdependences between subcellular locations and incorporates them with support vector machines for prediction of protein subcellular localisation. Traditional prediction systems utilise a 'flat' structure of classifiers, such as the one-versus-all and one-versus-one schemes, with amino acid compositions to perform the prediction. Apart from those existing studies that ignore the interdependences between subcellular locations, we take advantage of a hierarchical structure to organise the subcellular locations and model their relationships. Here, we propose to use four kinds of hierarchical prediction methods and make comparative studies on three datasets. Experimental results show that three of the hierarchical models outperform the traditional 'flat' model in terms of tree loss values. In particular, one hierarchical model outperforms the traditional 'flat' model for all evaluation measures. Moreover, we gained some valuable insights into the sorting process by using hierarchical structures. © 2011 Taylor & Francis.

关键词： Support vector machines

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Rank-SIFT: Learning to rank repeatable local interest points

Rank-SIFT: Learning to rank repeatable local interest points

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作者： Li, Bing Xiao, Rong Li, Zhiwei Cai, Rui Lu, Bao-Liang Zhang, Lei Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Microsoft Research Asia Beijing 100190 China MOE Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781457703942

Scale-invariant feature transform (SIFT) has been well studied in recent years. Most related research efforts focused on designing and learning effective descriptors to characterize a local interest point. However, how to identify stable local interest points is still a very challenging problem. In this paper, we propose a set of differential features, and based on them we adopt a data-driven approach to learn a ranking function to sort local interest points according to their stabilities across images containing the same visual objects. Compared with the handcrafted rule-based method used by the standard SIFT algorithm, our algorithm substantially improves the stability of detected local interest point on a very challenging benchmark dataset, in which images were generated under very different imaging conditions. Experimental results on the Oxford and PASCAL databases further demonstrate the superior performance of the proposed algorithm on both object image retrieval and category recognition. © 2011 IEEE.

关键词： Image retrieval

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