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Cost-sensitive multi-label learning with positive and negative label pairwise correlations

NEURAL NETWORKS 2018年 108卷 411-423页

作者： Wu, Guoqiang Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

Multi-label learning is the problem where each instance is associated with multiple labels simultaneously. Binary Relevance (BR) is a representative algorithm for multi-label learning. However, it may suffer the class-imbalance issue especially when the label space is large. Besides, it ignores the label correlations, which is of importance to improve the performance. Moreover, labels might have positive and negative correlations in real applications, but existing methods seldom exploit the negative label correlations. In this paper, we propose a novel Cost-sensitive multi-label learning model with Positive and Negative label pairwise correlations (CPNL), which extends BR to tackle the above issues. The kernel extension of the linear model is also provided to explore complex input-output relationships. Moreover, we adopt two accelerated gradient methods (AGM) to efficiently solve the linear and kernel models. Experimental results show that our approach CPNL achieves a competitive performance to some state-of-the-art approaches for multi-label learning. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Binary Relevance Multi-label learning Cost-sensitive Positive label correlations Negative label correlations

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Fixed-income securities: bibliometric review with network analysis

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SCIENTOMETRICS 2018年第3期116卷 1615-1640页

作者： Yan, Yan Liao, Zhewen Chen, Xiaosong Univ Chinese Acad Sci Sch Econ & Management Beijing 100080 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Inst Theoret Phys Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Phys Sci Beijing 100080 Peoples R China

In the financial market, many fixed-income analysts focus on trajectories of the hotspots. However, limited attention has been paid to the quantitative and qualitative evolution in a rapidly changing research field of fixed-income securities. This paper identifies intellectual turning points and emerging trends in the fixed-income securities field by using bibliometric methods, visualizing complicated networks including co-keyword networks, journal co-citation networks. Highly cited terms, categories, countries and institutes are detected and discussed. By using all 5915 publications published between 1997 and 2016, recorded in SCI core collection and SSCI in fixed-income securities domain and their references (101911), it can be conducted from this research that 10 top journals in the fixed-income field. The output of this article also shows the pioneer authors, the leading institutes and leading countries and regions, and what's more, the trend of research in fixed-income securities domain. It could possibly be a valuable source for academics and practitioners working in the field of fixed-income securities.

关键词： Bonds Fixed-income securities Bibliometrics Network visualization

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Multi-view learning based on nonparallel support vector machine

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knowledge-BASED SYSTEMS 2018年 158卷 94-108页

作者： Tang, Jingjing Li, Dewei Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

Multi-view learning (MVL) focuses on the problem of learning from the data represented by multiple distinct feature sets. Various successful SVM-based multi-view learning models have been proposed to improve existing learning tasks performance. Since nonparallel support vector machine (NPSVM) is proposed with several incomparable advantages over the state-of-the-art classifiers, it is potentially beneficial to perform the multi-view classification task using NPSVM. In this paper, we build a new multi-view learning model based on nonparallel support vector machine, termed as MVNPSVM. By combining the large margin mechanism and the consensus principle, MVNPSVM not only inherits the advantages of both NPSVM and multi-view learning, but also brings a new insight of extending NPSVM to the multi-view learning field. To solve MVNPSVM efficiently, we adopt the alternating direction method of multipliers (ADMM) as the solution. We theoretically analyze the performance of MVNPSVM from the viewpoints of the consensus analysis and the comparisons with the other two similar methods SVM-2K and multi-view twin support vector machines. Experimental results on 95 binary data sets confirm the effectiveness of the proposed method.

关键词： Multi-view learning Nonparallel support vector machine Alternating direction method of multipliers

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What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization

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ECLINICALMEDICINE 2020年 22卷 100354-100354页

作者： Liu, Yang Gu, Zhonglei Xia, Shang Shi, Benyun Zhou, Xiao-Nong Shi, Yong Liu, Jiming Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China HKBU CSD & NIPD Joint Res Lab Intelligent Dis Sur Hong Kong Peoples R China Chinese Ctr Dis Control & Prevent Natl Inst Parasit Dis Chinese Ctr Trop Dis Res Shanghai Peoples R China Natl Hlth & Commiss Peoples Republ China Key Lab Parasite & Vector Biol Shanghai Peoples R China WHO Collaborating Ctr Trop Dis Shanghai Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Background: COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries. Methods: In this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0-6 years old (children);7-14 (primary and junior high school students);15-17 (high school students);18-22 (university students);23 -44 (young/middle-aged people);45-64 years old (middle-aged/elderly people);and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households;(2) schools, including primary/high schools as well as colleges and universities;(3) various physical workplaces;and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations. Findings: We focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19 in China, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedl

关键词： COVID-19 Underlying transmission patterns Age-specific social contact patterns Retrospective and prospective analysis

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Finding Patterns of Stock Returns Based on Sequence Alignment 6th

Finding Patterns of Stock Returns Based on Sequence Alignmen...

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6th International Conference on Information Technology and Quantitative management (ITQM)

作者： Shi, Yong Tang, Ye-ran Long, Wen Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

In this paper, we propose the method based on sequence alignment to find patterns of stock returns. We use 5 minutes high frequency data of CSI 300 index to test this method, and fmd we can predict the sharply rise or drop for the stock returns according to patterns of the sample symbol sequence. The empirical analysis suggests it is possible to fmd and predict patterns in stock returns based on the method of sequence alignment. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： finding patterns sequence alignment stock returns

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Bilevel Multiview Latent Space Learning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2018年第2期28卷 327-341页

作者： Xue, Zhe Li, Guorong Wang, Shuhui Zhang, Weigang Huang, Qingming Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 101408 Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing 101408 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Beijing 100049 Peoples R China

Different kinds of features describe different aspects of image data, and each feature can be treated as a view when we take it as a particular understanding of images. Leveraging multiple views provides a richer and comprehensive description than using only a single view. However, multiview data are often represented by high-dimensional heterogeneous features, so it is meaningful to find a low-dimensional consensus representation from multiple views. In this paper, we propose an unsupervised multiview dimensionality reduction method for images based on bilevel latent space learning. As different views have different physical meanings and statistical properties, they are not directly comparable. Therefore, we learn the comparable representation for each view in the first level. The shared and the private nature of multiview data are exploited to accurately preserve the information of each view. Then, we fuse different views into a low-dimensional representation by conducting joint matrix factorization in the second level. To guarantee the low-dimensional representation to be compact and discriminative, the intrinsic geometric structure of data is utilized. Besides, our method considers resisting the outliers and noise contained in multiview data, which may influence the learned representation and deteriorate its semantic consistency. We design appropriate optimization objectives to learn the latent spaces in different levels. Compared with the existing methods, our method could provide a more flexible multiview learning strategy that not only accurately captures the information of each view but also is robust to outliers and noise, which can obtain a more discriminative and compact low-dimensional representation. Experiments on two real-world image data sets demonstrate the advantages of our method over the existing multiview dimensionality reduction methods.

关键词： Image and video classification latent space matrix factorization multiview

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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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Learning from label proportions on high-dimensional data

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NEURAL NETWORKS 2018年 103卷 9-18页

作者： Shi, Yong Liu, Jiabin Qi, Zhiquan Wang, Bo Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Learning from label proportions (LLP), in which the training data is in the form of bags and only the proportion of each class in each bag is available, has attracted wide interest in machine learning. However, how to solve high-dimensional LLP problem is still a challenging task. In this paper, we propose a novel algorithm called learning from label proportions based on random forests (LLP-RF), which has the advantage of dealing with high-dimensional LLP problem. First, by defining the hidden class labels inside target bags as random variables, we formulate a robust loss function based on random forests and take the corresponding proportion information into LLP-RF by penalizing the difference between the ground truth and estimated label proportion. Second, a simple but efficient alternating annealing method is employed to solve the corresponding optimization model. At last, various experiments demonstrate that our algorithm can obtain the best accuracies on high-dimensional data compared with several recently developed methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Optimization High-dimensional data Learning from label proportions (LLP) Random forests

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An Efficient Slap Fingerprint Segmentation Algorithm Based on Convnets and Knuckle Line 1

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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: (数字)9783319699233

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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