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Analyzing Health Seeking Behavior of Chinese Residents and Their Influencing Factors Based on CHNS data

Procedia Computer Science 2019年 162卷 835-841页

作者： Jiayu Xue Xinyue Ren Yuanjie Xu Qianqian Feng School of Economics and Management 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 Sino-Danish College University of Chinese Academy of Sciences Beijing China Institutes of Science and Development Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

With the improvement of people’s living standards, Chinese residents have paid more and more attention to health. The development of science and technology has made medical health big data emerge and has gradually become a research hotspot. In our paper, we first adopted Logistic Regression to find out the important variables for health seeking behavior and then we verified that this behavior can be predicted based on machine learning algorithms. By comparing classic Support Vector Machine (SVM) algorithm and improved SVM model with different variables, we finally proposed a SVM model based on SMOTE algorithm that is relatively optimal for predicting health seeking behavior.

关键词： Health seeking behavior Logistic Regression Support Vector Machine SMOTE

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Multi-view SVM Classification with Feature Selection

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Procedia Computer Science 2019年 162卷 405-412页

作者： Yuting Niu Yuan Shang Yingjie Tian School of Information Engineering Zhengzhou University Zhengzhou 450001 China Smart City Institute Zhengzhou University Zhengzhou 450001 China Supercomputer Center Smart City Institute Zhengzhou University Henan 450001 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge management Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l 1 norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model.

关键词： multi-view learning classification feature selection sparsity regularization linear programming

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The dynamical relationship between capital market and macroeconomy: based on dynamic Bayesian network

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Procedia Computer Science 2019年 162卷 46-52页

作者： Yue Liu Yijing Wang Guihuan Zheng Jue Wang Kun Guo School of Economics and Management University of Chinese Academy of Sciences Beijing and 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing and 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing and 100190 China China Institute Of Finance And Capital Markets China Securities Regulatory Commission Beijing and 100032 China The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing and 100190 China

The stock market volatility will be affected by many economic factors. On the contrary, the stock market will also influence the formulation of the economy and policy. It will be very useful if a qualitative structure can be computed to figure out the corresponding level between the stock market with these factors. The dynamical Bayesian factor graph is a suitable method to solve this problem. We choose a series of economic factors to represent the possible influence from macroeconomy, and macro-policy. We have found that the changes in the stock market during the bull market will affect the macroeconomy factors, and this effect will continue to weaken as the stock market moves to the bear market.

关键词： Dynamical Bayesian factor graph Macroeconomic factors Macro-policy factors Stock market

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Review of bankruptcy prediction using machine learning and deep learning techniques

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Procedia Computer Science 2019年 162卷 895-899页

作者： Yi Qu Pei Quan Minglong Lei Yong Shi School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Faculty of Information Technology Beijing University of Technology Beijing 100124 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Bankruptcy prediction has long been a significant issue in finance and management science, which attracts the attention of researchers and practitioners. With the great development of modern information technology, it has evolved into using machine learning or deep learning algorithms to do the prediction, from the initial analysis of financial statements. In this paper, we will review the machine learning or deep learning models used in bankruptcy prediction, including the classical machine learning models such as Multivariant Discriminant Analysis (MDA), Logistic Regression (LR), Ensemble method, Neural Networks (NN) and Support Vector Machines (SVM), and major deep learning methods such as Deep Belief Network (DBN) and Convolutional Neural Network (CNN). In each model, the specific process of experiment and characteristics will be summarized through analyzing some typical articles. Finally, possible innovative changes of bankruptcy prediction and its future trends will be discussed.

关键词： Bankruptcy prediction Machine learning Deep learning

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New Discoveries of Domination Between Traffic Matrices

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 561-566页

作者： Peng-fei LIU Wen-guo YANG Tian-de GUO School of Mathematics Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China

In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominates D2. Let u（D） be the set of all the traffic matrices that are dominated by the traffic matrix D. It is shown that u （ D∞） and u （D ∈） are isomorphic. Besides, similar results are obtained on multi-commodity flow problems. Fhrthermore, the results are the generalized to integral flows.

关键词： robust network design multi-commodity flows domination

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ANALYSIS OF THE PREDICTION CAPABILITY OF WEB SEARCH data BASED ON THE HE-TDC METHOD - PREDICTION OF THE VOLUME OF DAILY TOURISM VISITORS

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Journal of Systems Science and Systems Engineering 2017年第2期26卷 163-182页

作者： Geng Peng Ying Liu Jiyuan Wang Jifa Gu School of Economic and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

Web search query data are obtained to reflect social spots and serve as novel economic indicators. When faced with high-dimensional query data, selecting keywords that have plausible predictive ability and can reduce dimensionality is critical. This paper presents a new integrative method that combines Hurst Exponent （HE） and Time Difference Correlation （TDC） analysis to select keywords with powerful predictive ability. The method is called the HE-TDC screening method and requires keywords with predictive ability to satisfy two characteristics, namely, high correlation and fluctuation memorability similar to the predicting target series. An empirical study is employed to predict the volume of tourism visitors in the Jiuzhai Valley scenic area. The study shows that keywords selected using HE-TDC method produce a model with better robustness and predictive ability.

关键词： Tourism visitor volume prediction web-search data HE-TDC method Jiuzhai Valley,time series Hurst exponent

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An Efficient Slap Fingerprint Segmentation Algorithm Based on Convnets and Knuckle Line 12th

An Efficient Slap Fingerprint Segmentation Algorithm Based o...

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12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Tang, Siqi Qin, Jin Liu, Yan Han, Congying Guo, Tiande Beijing China Key Laboratory of Big Data Mining and Knowledge Management School of Mathematical Science UCAS Beijing China

ISBN: (纸本)9783319699226

We propose a novel and efficient technique to extract individual fingerprints from a slap-image and identify them into their corresponding indices i.e. index, middle, ring or little finger of left/right hand. We pose the orientation of the hand to a classification problem, and present an approach based on Convolutional Neural Networks (ConvNets) to address the angle of the hand. Geometrical and spatial properties of hand are applied to split a single finger and detect the knuckle line. The proposed algorithm solves the challenges of segmentation like the large rotational angles of the hand and non-elliptical shape of components. Extensive experimental evaluations demonstrate the success of this approach. © 2017, Springer International Publishing AG.

关键词： Convolution

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A Novel Texture Generation Super Resolution Model

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Procedia Computer Science 2019年 162卷 924-931页

作者： Biao Li Yong Shi Sujuan Li Bo Wang Zhiquan Qi Jiabin Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA Department of Otology the First Affiliated Hospital of Zhengzhou University No. 1 eastern Jianshe road Zhengzhou Henan 450052 China School of Information Technology and Management University of International Business and Economics Beijing 100029 China Department of Computer Science and Engineering Texas A&M University TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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s-LWSR: Super lightweight super-resolution network

arXiv

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arXiv 2019年

作者： Li, Biao Liu, Jiabin Wang, Bo Qi, Zhiquan Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Information Technology and Management University of International Business and Economics Beijing100029 China Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States College of Information Science and Technology University of Nebraska OmahaNE68182 United States

Deep learning (DL) architectures for super-resolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification

arXiv

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arXiv 2019年

作者： Wu, Guoqiang Zheng, Ruobing Tian, Yingjie Liu, Dalian School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge management Chinese Academy of Sciences Beijing100190 China Department of Basic Course Teaching Beijing Union University Beijing100101 China

Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style way for thresholding, it may suffer error accumulation and thus reduces the final classification performance. Binary Relevance (BR) is another typical method, which aims to minimize the Hamming Loss and only needs one-step learning. Nevertheless, it might have the class-imbalance issue and doesn’t take into account label correlations. To address the above issues, we propose a novel multi-label classification model, which joints Ranking support vector machineNovember 6, 2019 and Binary Relevance with robust Low-rank learning (RBRL). RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations. Meanwhile, it utilizes the hamming loss minimization and one-step learning advantages of BR, and thus tackles the disadvantages of Rank-SVM including another thresholding learning step. Besides, a low-rank constraint is utilized to further exploit high-order label correlations under the assumption of low dimensional label space. Furthermore, to achieve nonlinear multi-label classifiers, we derive the kernelization RBRL. Two accelerated proximal gradient methods (APG) are used to solve the optimization problems efficiently. Extensive comparative experiments with several state-of-the-art methods illustrate a highly competitive or superior performance of our method RBRL. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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