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ONP-Miner: One-off Negative Sequential Pattern mining

ACM TRANSACTIONS ON knowledge DISCOVERY FROM data 2022年第3期17卷 1-24页

作者： Wu, Youxi Chen, Mingjie Li, Yan Liu, Jing Li, Zhao Li, Jinyan Wu, Xindong Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Key Lab Big Data Comp Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Zhejiang Univ Alibaba ZJU Joint Res Inst Frontier Technol Hangzhou 310000 Peoples R China Univ Technol Sydney Data Sci Inst Sydney NSW Australia Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China

Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this article discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this article proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency but also has better mining performance than the stateof-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.

关键词： Sequential pattern mining negative sequential pattern one-off condition gap constraint

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A dynamic key node learning algorithm in evolving financial network: case study during the period of coronavirus

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Procedia Computer Science 2022年 214卷 1587-1594页

作者： Doudou Lu Wei Deng Yongyin Xu Yong Shi College of Big Data Statistics Guizhou University of Finance and Economics Guizhou 550025 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Finding an influential set of nodes in the interconnected network, whose activities would maximally influence the development of overall network, is a fundamental topic in finance, biology, social science, epidemic control and other scenarios with respect to network science. In recent years, some works are proposed to find key nodes via network connectivity measures, these studies assume a static environment, and besides, key nodes are calculated through pairwise connectivity, the number of connected components and other measures from the perspective of graph theory. However, these studies leave out of consideration on the dynamic evolving environment and causal relationships in the network. In this paper, we introduce a key node learning algorithm in an evolving financial network. The intuition of the algorithm is to model the causality of financial entities over the time, and the metrics for cascading networks are proposed to grade the entities in the network. In the experiment, the worldwide financial activities before and during the period of coronavirus are analyzed. The proposed metrics can effectively capture the influential entities and quantitatively describe these influences on the whole financial system.

关键词： COVID-19 stock market indices high-impact entities

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Adaboost-LLP: A Boosting Method for Learning With label Proportions

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第8期29卷 3548-3559页

作者： Qi, Zhiquan Meng, Fan Tian, Yingjie Niu, Lingfeng Shi, Yong Zhang, Peng Chinese Acad Sci Res Ctr Fictitious Econ & Data 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 Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100081 Peoples R China Ant Financial Serv Grp Hangzhou 310000 Zhejiang Peoples R China

How to solve the classification problem with only label proportions has recently drawn increasing attention in the machine learning field. In this paper, we propose an ensemble learning strategy to deal with the learning problem with label proportions (LLP). In detail, we first give a loss function based on different weights for LLP, and then construct the corresponding weak classifier, at the same time, estimate its conditional probabilities by a standard logistic function. At last, by introducing the maximum likelihood estimation, we propose a new anyboost learning system for LLP (called Adaboost-LLP). Unlike traditional methods, our method does not make any restrictive assumptions on training set;at the same time, compared with alter-proportional to SVM, Adaboost-LLP exploits more extra weight information and uses multiple weak classifiers that can be solved efficiently to combine a strong classifier. All experiments show that our method outperforms the existing methods in both accuracy and training time.

关键词： Classification ensemble learning learning with label proportions machine learning

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A nominal association matrix with feature selection for categorical data

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第16期46卷 7798-7819页

作者： Huang, Wenxue Shi, Yong Wang, Xiaogang Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Guangdong Peoples R China Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China York Univ Dept Math & Stat Toronto ON Canada

An intrinsic association matrix is introduced to measure category-to-variable association based on proportional reduction of prediction error by an explanatory variable. The normalization of the diagonal gives rise to the expected rates of error-reduction and the off-diagonal yields expected distributions of the rates of error for all response categories. A general framework of association measures based on the proposed matrix is established using an application-specific weight vector. A hierarchy of equivalence relations defined by the association matrix and vector is shown. Applications to financial and survey data together with simulation results are presented.

关键词： Association matrixl Association vector Categorical data Feature selection Proportional prediction The GK-tau

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Which kind of directed technical change does China's economy have? From the perspective of energy-saving and low-carbon

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JOURNAL OF CLEANER PRODUCTION 2019年 233卷 160-168页

作者： Xiu, Jing Zhang, Guo-xing Hu, Yi Lanzhou Univ Sch Management Lanzhou 730000 Gansu Peoples R China Jilin Acad Social Sci Inst Econ Changchun 130033 Jilin 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

Improvements in energy-saving and low-carbon technologies in the economic system are foundations of sustainable economic development. Therefore, measuring the bias of technical change from the perspective of energy-saving and low-carbon is a prerequisite for clarifying whether economic growth is manifested as low-carbon and sustainable. In this paper, the production function from the perspective of energy-saving and low-carbon in China is constructed, and ridge regression is used to estimate it to measure energy and carbon bias in China's technical change. The results indicate that technical change is biased towards energy use and carbon emissions relative to capital and labor, but this bias is weakening. In general, China's economic system has always biased towards carbon emissions, and its carbon-biased technical change has the largest share in directed technical change. However, it is gradually shrinking. (C) 2019 Published by Elsevier Ltd.

关键词： Energy-saving and low-carbon Directed technical change Ridge regression Sustainable economic development

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FedPS: A Privacy Protection Enhanced Personalized Search Framework 21

FedPS: A Privacy Protection Enhanced Personalized Search Fra...

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30th World Wide Web Conference (WWW)

作者： Yao, Jing Dou, Zhicheng Wen, Ji-Rong Renmin Univ China Sch Informat Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9781450383127

Personalized search returns each user more accurate results by collecting the user's historical search behaviors to infer her interests and query intents. However, it brings the risk of user privacy leakage, and this may greatly limit the practical application of personalized search. In this paper, we focus on the problem of privacy protection in personalized search, and propose a privacy protection enhanced personalized search framework, denoted with FedPS. Under this framework, we keep each user's private data on her individual client, and train a shared personalized ranking model with all users' decentralized data by means of federated learning. We implement two models within the framework: the first one applies a personalization model with a personal module that fits the user's data distribution to alleviate the challenge of data heterogeneity in federated learning;the second model introduces trustworthy proxies and group servers to solve the problems of limited communication, performance bottleneck and privacy attack for FedPS. Experimental results verify that our proposed framework can enhance privacy protection without losing too much accuracy.

关键词： personalized search privacy protection federated learning

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A Comprehensive Survey of Loss Functions in Machine Learning

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Annals of data Science 2022年第2期9卷 187-212页

作者： Wang, Qi Ma, Yue Zhao, Kun Tian, Yingjie School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 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 Logistics Beijing Wuzi University Beijing101149 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

As one of the important research topics in machine learning, loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has a big gap to summarize, analyze and compare the classical loss functions. Therefore, this paper summarizes and analyzes 31 classical loss functions in machine learning. Specifically, we describe the loss functions from the aspects of traditional machine learning and deep learning respectively. The former is divided into classification problem, regression problem and unsupervised learning according to the task type. The latter is subdivided according to the application scenario, and here we mainly select object detection and face recognition to introduces their loss functions. In each task or application, in addition to analyzing each loss function from formula, meaning, image and algorithm, the loss functions under the same task or application are also summarized and compared to deepen the understanding and provide help for the selection and improvement of loss function. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Surveys

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How China Deals with COVID-19 Pandemic

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第1期23卷 17-35页

作者： Shi, Yong Qu, Yi Zhang, Linzi 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 Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

Currently, the whole world is still suffering from the COVID-19 (variants) pandemic that has been lasting for over three years and resulted in countless losses. Since its outbreak at the end of 2019, China has first suffered, but quickly gained the upper hand through strict epidemic control and emergency management. Despite several recurrences of regional epidemics in 2022, both economic growth and people's livelihoods have been effectively safeguarded in China, with a regular prevention system established and social life recovery accomplished orderly. How does China deal with this pandemic? What practical measures taken and decisions made could be adopted and shared with other countries? In this paper, these issues are summarized and discussed as progressive lessons for the rest of the world. Though there's a major shift of pandemic policy in late 2022 and early 2023 in China, this study still provides insightful and beneficial implications to support decision- and policy-makings in future fights against any possible COVID-19 variants until the end of the pandemic.

关键词： COVID-19 pandemic China emergency management strategic decision making

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A new graphic kernel method of stock price trend prediction based on financial news semantic and structural similarity

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 118卷 411-424页

作者： Long, Wen Song, Linqiu Tian, Yingjie Univ Chinese Acad Sci Sch Econ & Management 80 Zhongguancun East St Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci 80 Zhongguancun East St Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhongguancun East St Beijing 100190 Peoples R China

Lots of researches try to predict the stock price movement using financial news based on machine learning represented by SVM (Support Vector Machine). But almost all of them focus on the news contents while very few consider the information hiding in the relationship between different news. In this paper, we proposed a new kernel based on SVM concerning not only the contents themselves but also the information structures among them. As both the news contents and the information structures are imported into our kernel, this kernel is named as semantic and structural kernel, referred to S&S kernel. Medical industry financial news is used to illustrate the efficiency of our kernel. By comparing the predicting accuracy of S&S kernel with other kernels, such as linear kernel, we find our method outperforms the others by at least 5% on accuracy, which is a quite meaningful promotion. The result also confirms the information structure contained in daily financial news can offer extra information helping to predict the trend of stock price. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Stock price movement prediction Financial news Information structure S&S kernel

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Ramp loss one-class support vector machine;A robust and effective approach to anomaly detection problems

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NEUROCOMPUTING 2018年 310卷 223-235页

作者： Tian, Yingjie Mirzabagheri, Mahboubeh Bamakan, Seyed Mojtaba Hosseini Wang, Huadong Qu, Qiang Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 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 Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Anomaly detection defines as a problem of finding those data samples, which do not follow the patterns of the majority of data points. Among the variety of methods and algorithms proposed to deal with this problem, boundary based methods include One-class support vector machine (OC-SVM) is considered as an effective and outstanding one. Nevertheless, extremely sensitivity to the presence of outliers and noises in the training set is considered as an important drawback of this group of classifiers. In this paper, we address this problem by developing a robust and sparse methodology for anomaly detection by introducing Ramp loss function to the original One-class SVM, called "Ramp-OCSVM". The main objective of this research is to taking the advantages of non-convexity properties of the Ramp loss function to make robust and sparse semi-supervised algorithm. Furthermore, the Concave-Convex Procedure (CCCP) is utilized to solve the obtained model that is a non-differentiable non-convex optimization problem. We do comprehensive experiments and parameters sensitivity analysis on two artificial data sets and some chosen data sets from UCI repository, to show the superiority of our model in terms of detection power and sparsity. Moreover, some evaluations are done with NSL-KDD and UNSW-NB15 data sets as well-known and recently published intrusion detection data sets, respectively. The obtained results reveal the outperforming of our model in terms of robustness to outliers and superiority in the detection of anomalies. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Anomaly detection One-class SVM Ramp loss function Non-convex problem

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