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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Wen Long Huiwen Wang Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China School of Economics and Management Beihang University Beijing China

It is necessary to take much time and resources for compiling an input-output table, and there inevitably exists time lag in all the finished tables, which has greatly weakened the analysis and application function of input-output table. Though the development of techniques of adjusting input coefficient has relieved disadvantage of time lag to some extent, yet this kind of methods is still inapplicable to predictive problem. Consider that internal constraints of the table, this paper proposes a new approach based on the sequential tables series to solve the problem of forecasting the future input-output table.

关键词： Mathematical model Economics Forecasting Predictive models Matrix converters Industries

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Group sensitive Classifier Chains for multi-label classification

Group sensitive Classifier Chains for multi-label classifica...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jun Huang Guorong Li Shuhui Wang Weigang Zhang Qingming Huang Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences ICT CAS Key Lab of Intelligent Information Processing (CAS) Beijing China Harbin Institute of Technology at Weihai Weihai China

In multi-label classification, labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the global label correlations. However, the label correlations may be different over different data groups. In this paper, we propose a simple and efficient framework for multi-label classification, called Group sensitive Classifier Chains. We assume that similar examples not only share the same label correlations, but also tend to have similar labels. We augment the original feature space with label space and cluster them into groups, then learn the label dependency graph in each group respectively and build the classifier chains on each group specific label dependency graph. The group specific classifier chains which are built on the nearest group of the test example are used for prediction. Comparison results with the state-of-the-art approaches manifest competitive performances of our method.

关键词： Correlation Birds Accuracy Boats Training data Training Measurement

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Market Value of Innovation: An Empirical Analysis on China's Stock Market

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Procedia Computer Science 2015年 55卷 1275-1284页

作者： Meng Xiangying Zhang Yueyan Wei Xianhua School of Management University of Chinese Academy Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science The State Key Laboratory of Automotive Safety and Energy Tsinghua University Department of Automotive Engineering Tsinghua University Beijing 100084 China

Studies on market value of innovation investment have been based on US and European database. However, few re-searchers investigate Chinese stock market about the market value of firm's expected R&D performance. In this paper, we use constructed panel dataset from three representative stock markets in China: Main Board Market, Small and Media-sized Enterprise Board Market and Growth Enterprise Market and then use intangible assets increment as innovation indicator to examine the innovation investment-firm performance association in both manufacturing and service industries by a dataset over an 11-year period covering 1455 firms to evaluate the differences in their relative contribution to market performance. By comparing the results of these three stock markets, we find out the effect of R&D investment and increasing R&D input to market value is insignificant in all three stock markets, resulting from the weak protection for minority investors and loose regulation of information disclosure. Different constructions of intangible assets of listed firms on MBM and GEM account for market's relatively low efficiency to reflect real value of R&D investment. On industry-level, we conclude that R&D investment in both service and manufacturing sector contributes positively to market performance of firms and R&D investment in service industry shows stronger and more significant linkage to market value than manufacturing industry.

关键词： Market value Innovation R&D China's stock market

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f-Fractional Bit Minwise Hashing for Large-Scale Learning

f-Fractional Bit Minwise Hashing for Large-Scale Learning

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jingjing Tang Yingjie Tian School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge management Beijing China

Recently, document similarity detection technology captures a host of researchers' attention. In this paper, we propose to integrate linear SVM with f-fractional bit minwise hashing to make a wide range of choices for accuracy and storage space requirements. According to the derived properties of f-fractional bit minwise hashing, we obtained the optimal combination of fractional bit with the minimum estimator of variances and ultimately applied it to the process of integration. The innovation of this algorithm is the continuous selectivity of bit instead of the discrete integer value, which not only improves the theoretical system of b-bit minwise hashing SVM algorithm, but also satisfies the various needs of accuracy and storage space in the practical system. Due to the nonlinear of the resemblance matrix considered as kernel matrix, it can not be used in linear SVM training directly. However, in the theoretical analysis, we provide the proof of positive definiteness for resemblance matrix generated by f-fractional bit minwise hashing scheme, which is a logical and feasible basis for the integration. Meanwhile, experimental results on publicly available large-scale datasets validate the effectiveness of this algorithm.

关键词： Support vector machines Algorithm design and analysis Optimized production technology Radio frequency Matrix decomposition Matrix converters Kernel

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Image Segmentation via Improving Clustering Algorithms with Density and Distance

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Procedia Computer Science 2015年 55卷 1015-1022页

作者： Zhensong Chen Zhiquan Qi Fan Meng Limeng Cui Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing 100190 China Sino-Danish Center for Education and Research Beijing 100190 China Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Image segmentation problem is a fundamental task and process in computer vision and image processing applications. It is well known that the performance of image segmentation is mainly influenced by two factors: the segmentation approaches and the feature presentation. As for image segmentation methods, clustering algorithm is one of the most popular approaches. However, most current clustering-based segmentation methods exist some problems, such as the number of regions of image have to be given prior, the different initial cluster centers will produce different segmentation results and so on. In this paper, we present a novel image segmentation approach based on DP clustering algorithm. Compared with the current methods, our method has several improved advantages as follows: 1) This algorithm could directly give the cluster number of the image based on the decision graph; 2) The cluster centers could be identified correctly; 3) We could simply achieve the hierarchical segmentation according to the applications requirement. A lot of experiments demonstrate the validity of this novel segmentation algorithm.

关键词： Image Segmentation Clustering Algorithm Feature Representation

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Analysis on Marketing Ability and Financial Performance in Internet Company

Analysis on Marketing Ability and Financial Performance in I...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Zhuofan Yang Yong Shi Chinese Academy Sciences Research Center on Fictitious Economy & Data Science Beijing China Chinese Academy Sciences Key Research Laboratory of Big Data Mining and Knowledge Management Beijing China

This paper measures marketing ability and financial performance and examines what cause the overall operations inefficiency based on the operational process decomposition. The empirical research on e-portal and e-commerce company show that e-portal company has better marketing ability while e-commerce firm has better financial performance. For e-commerce company, it is marketing ability conversion inefficiency to lead to the overall operations inefficiency. In contrast, for e-portal company, lower financial performance conversion efficiency causes the overall operations inefficiency.

关键词： Companies Internet Social network services Portals Analytical models Couplings data models

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A Comprehensive Survey of Clustering Algorithms

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Annals of data Science 2015年第2期2卷 165-193页

作者： Dongkuan Xu Yingjie Tian School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

关键词：

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Search Engine Marketing, Financing Ability and Firm Performance in E-commerce

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Procedia Computer Science 2015年 55卷 1106-1112页

作者： Zhuofan Yang Yong Shi Bo Wang Management of School University of Chinese Academy Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy Sciences Beijing 100190 China Key Research Laboratory of Big Data Mining and Knowledge Management Chinese Academy Sciences Beijing 100190 China

This paper explores the relationship of search engine marketing, financing ability and e-commerce firm performance by the empirical research on China's B2C e-commerce firms. Results show that search engine marketing and business model has a strong positive relation with firm performance while financing ability has a negative effect on firm performance. It verifies the low returns to inputs in e-commerce and enlightens the managers should concentrate on business model innovation and consumer relation management.

关键词： Search Engine Marketing E-commerce Financing ability Firm performance

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Large-Scale Linear Support Vector Ordinal Regression Solver

Large-Scale Linear Support Vector Ordinal Regression Solver

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Yong Shi Huadong Wang Lingfeng Niu Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China University Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467384940

In multiple classification, there is a type of commonproblems where each instance is associated with an ordinal label, which arises in various settings such as text mining, visual recognition and other information retrieval tasks. The support vectorordinal regression (SVOR) is a good model widely used for ordinalregression. In some applications such as document classification, data usually appears in a high dimensional feature space andlinear SVOR becomes a good choice. In this work, we developan efficient solver for training large-scale linear SVOR basedon alternating direction method of multipliers(ADMM). Whencompared empirically on benchmark data sets, the proposedsolver enjoys advantages in terms of both training speed andgeneralization performance over the method based on SMO, which invalidate the effectiveness and efficiency of our algorithm.

关键词： Support vector machines Training Prediction algorithms Machine learning algorithms big data Measurement Optimization

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A New Intrusion Detection Approach Using PSO based Multiple Criteria Linear Programming

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Procedia Computer Science 2015年 55卷 231-237页

作者： Seyed Mojtaba Hosseini Bamakan Behnam Amiri Mahboubeh Mirzabagheri Yong Shi Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100090 China Department of IT management University of Tehran Tehran Iran College of Information Science and Technology University of Nebraska at Omaha 68182 NE USA

Intrusion detection system (IDS) is an inseparable part of each computer networks to monitor the events and attacks, which many researchers proposed variety of models to improve the performance of IDS. In this paper we present a new method based on multiple criteria linear programming and particle swarm optimization to enhance the accuracy of attacks detection. Multiple criteria linear programming is a classification method based on mathematical programming which has been showed a potential ability to solve real-life data mining problems. However, tuning its parameters is an essential steps in training phase. Particle swarm optimization (PSO) is a robust and simple to implement optimization technique has been used in order to improve the performance of MCLP classifier. KDD CUP 99 dataset used to evaluate the performance of proposed method. The result demonstrated the proposed model has comparable performance based on detection rate, false alarm rate and running time compare to two other benchmark classifiers.

关键词： Network Intrusion detection Multiple criteria linear programming Particle swarm optimization Classification data mining

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