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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Yu, Anqi Peng, Shuyang Li, Yizhi Guo, Kun School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Dornslife School of Arts and Science University of Southern California Los AngelesCA90007 United States Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As global climate change intensifies, carbon emissions trading has become an important tool for countries to address climate change. China, as the world's largest carbon emitter, has been conducting carbon trading pilots since 2011, and officially launched a national carbon market in 2017. The construction of China's carbon market is crucial for China and global efforts to address climate change. Market efficiency is a key indicator of its success. This article aims to measure and analyze the effectiveness of China's carbon market, and to explore policy measures to enhance its effectiveness. It selected data from effective trading days and conducted an exploratory study on the effectiveness of China's pilot carbon markets. To analyze the effectiveness of the carbon market, we combined the efficient market hypothesis theory with the fractal market hypothesis theory, adopted the variance ratio test method, and the rescaled range analysis method to conduct a comprehensive analysis of the Chinese carbon market, while also accounting for the reasons behind their computational inconsistency. As our research shows that China's pilot carbon market has not reached the weak efficiency level, we have provided some suggestions based on the current operational status of China's carbon market. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Carbon

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Top-k Self-Adaptive Contrast Sequential Pattern mining

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11819-11833页

作者： Wu, Youxi Wang, Yuehua Li, Yan Zhu, Xingquan Wu, Xindong Hebei Univ Technol Sch Econ & Management Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230009 Peoples R China Mininglamp Acad Sci Beijing 100102 Peoples R China

For sequence classification, an important issue is to find discriminative features, where sequential pattern mining (SPM) is often used to find frequent patterns from sequences as features. To improve classification accuracy and pattern interpretability, contrast pattern mining emerges to discover patterns with high-contrast rates between different categories. To date, existing contrast SPM methods face many challenges, including excessive parameter selection and inefficient occurrences counting. To tackle these issues, this article proposes a top-k self-adaptive contrast SPM, which adaptively adjusts the gap constraints to find top-k self-adaptive contrast patterns (SCPs) from positive and negative sequences. One of the key tasks of the mining problem is to calculate the support (the number of occurrences) of a pattern in each sequence. To support efficient counting, we store all occurrences of a pattern in a special array in a Nettree, an extended tree structure with multiple roots and multiple parents. We employ the array to calculate the occurrences of all its superpatterns with one-way scanning to avoid redundant calculation. Meanwhile, because the contrast SPM problem does not satisfy the Apriori property, we propose Zero and Less strategies to prune candidate patterns and a Contrast-first mining strategy to select patterns with the highest contrast rate as the prefix subpattern and calculate the contrast rate of all its superpatterns. Experiments validate the efficiency of the proposed algorithm and show that contrast patterns significantly outperform frequent patterns for sequence classification. The algorithms and datasets can be downloaded from https://***/wuc567/Pattern-mining/tree/master/SCP-Miner.

关键词： databases Task analysis Electronic mail Time complexity Cybernetics Classification algorithms big data Apriori property contrast pattern self-adaptive sequence classification sequential pattern mining (SPM)

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NetDPO: (delta, gamma)-approximate pattern matching with gap constraints under one-off condition

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APPLIED INTELLIGENCE 2022年第11期52卷 12155-12174页

作者： Li, Yan Yu, Lei Liu, Jing Guo, Lei Wu, Youxi Wu, Xindong Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Hebei Key Lab Big Data Comp Tianjin 300401 Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230009 Peoples R China Mininglamp Technol Mininglamp Acad Sci Beijing 100084 Peoples R China

Approximate pattern matching not only is more general than exact pattern matching, but also allows some data noise. Most of them adopt the Hamming distance to measure similarity, which indicates the number of different characters in two sequences, but it cannot reflect the approximation between two characters. This paper addresses the approximate pattern matching with a local distance no larger than delta and a global distance no larger than gamma, which is named Delta and gamma Pattern matching with gap constraints under One-off condition (DPO). First, we show that the problem is an NP-Hard problem. Therefore, we construct a heuristic algorithm named approximate Nettree for DPO (NetDPO), which transforms the problem into an approximate Nettree based on delta distance which is a specially designed data structure. Then, NetDPO calculates the number of paths that reach the roots within gamma distance. To find the maximal occurrences, we employ the rightmost parent strategy and the optimal parent strategy to select the better occurrence which can minimize the influence after removing the occurrence. Iterate this process until there are no occurrences. Finally, we analyze the time and space complexities of NetDPO. Extensive experimental results verify the superiority of the proposed algorithm.

关键词： Pattern matching Pattern discovery Approximate pattern matching Gap constraint Nettree structure

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Do Media Reports Matter to House Price Expectations?Evidence from Beijing

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Journal of Systems Science and Information 2022年第4期10卷 388-409页

作者： Jing HE Diandian MA Xiuting LI Jichang DONG Postdoctoral Research Center of Agricultural Bank of China Beijing 100005China School of Statistics Renmin University of ChinaBeijing 100872China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

House price expectations(HPE)is a key factor affecting housing market *** Beijing as an example,this study innovatively proposes an internet data-based measurement of HPE and text analysis-based quantification of media reports from media attention and media *** model is performed to empirically test the impact of media reports on the level and accuracy of *** empirical results show no significant relationship between media attention and the level of HPE,but a significant relationship to the accuracy of *** higher the media attention(i.e.,the more intensive the media reports),the smaller the deviation between HPE and actual housing *** attitude of media reports is significantly related to the level and accuracy of *** is easier to guide the formation of HPE through media reports with clear opinions,indicating that media could promote the sustainable development of the real estate market.

关键词： media reports real estate market housing prices public expectations

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Recent advances in complementary label learning

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INFORMATION FUSION 2025年 114卷

作者： Tian, Yingjie Jiang, Haoran Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Coll Math & Sci 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 Chinese Acad Sci MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Complementary label Learning (CLL), a crucial aspect of weakly supervised learning, has seen significant theoretical and practical advancements. However, a comprehensive review of the field has been lacking. This survey provides the first exhaustive compilation and synthesis of state-of-the-art CLL approaches, filling a critical gap in the literature and serving as a foundational resource for the community. key contributions of this survey include an extensive categorization of CLL methodologies, clarifying diverse algorithms based on their principles and applications. This classification scheme enhances understanding of the CLL landscape, highlighting its versatility across varied settings. Additionally, the survey examines the evolution of CLL, showcasing its adaptability and potential in addressing complex applications. It also explores experimental frameworks, including processes for generating complementary labels and datasets and numerical evaluation of existing state-of-the-art. Moreover, the survey delves into how CLL integrates with and enhances other weakly supervised and semi-supervised learning approaches, deepening understanding of its role in the broader machine learning ecosystem. Overall, this survey not only compiles CLL research but also guides future explorations, steering the field towards new horizons in weakly supervised learning.

关键词： Complementary label learning Weakly supervised learning Machine learning

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knowledge Enhanced Search Result Diversification 22

Knowledge Enhanced Search Result Diversification

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28th ACM SIGKDD Conference on knowledge Discovery and data mining (KKD)

作者： Su, Zhan Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Univ Montreal Montreal PQ Canada Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450393850

Search result diversification focuses on reducing redundancy and improving subtopic richness in the results for a given query. Most existing approaches measure document diversity mainly based on text or pre-trained representations. However, some underlying relationships between the query and documents are difficult for the model to capture only from the content. Given that the knowledge base can offer well-defined entities and explicit relationships between entities, we exploit knowledge to model the relationship between documents and the query and propose a knowledge-enhanced search result diversification approach KEDIV. Concretely, we build a query-specific relation graph to model the complicated query-document relationship from an entity view. Then a graph neural network and node weight adjust algorithm are applied to the relation graph to obtain context-aware entity representations and document representations at each selection step. The diversity features are derived from the updated node representations of the relation graph. In this way, we can take advantage of entities' abundant information to model document's diversity in search result diversification. Experimental results on commonly used datasets show that our proposed approach can outperform the state-of-the-art methods.

关键词： Search Result Diversification knowledge Base Relation Graph

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ONP-Miner: One-off Negative Sequential Pattern mining

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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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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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Sequential Manipulation Against Rank Aggregation: Theory and Algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 9353-9370页

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Liu, Wei Cao, Xiaochun Sun, Yingfei Huang, Qingming Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China Tencent Data Platform Shenzhen 518054 Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management BDKM Sch Comp Sci & Technol Beijing 100049 Peoples R China

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner.

关键词： Online manipulation adversarial learning pairwise comparison ranking aggregation

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