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A neural network based forecasting method for the unemployment rate prediction using the search engine query data

A neural network based forecasting method for the unemployme...

引用

2011 8th IEEE International Conference on e-Business engineering, ICEBE 2011

作者： Xu, Wei Zheng, Tingting Li, Ziang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769545189

Unemployment rate prediction has become critically important, because it can help government to make decision and design policies. In recent years, forecast of unemployment rate attracts much attention from governments, organizations, and research institutes, and researchers. Recently, a novel method using search engine query data to forecast unemployment was proposed by scholars. In this paper, a data mining based framework using web information is introduced for unemployment rate prediction. Under the framework, a neural network method, as one of the most effective data mining tools, is developed to forecast unemployment trend using search engine query data. In the proposed method, search engine query data related with employment activities is firstly found. Secondly, feature selection models including correlation coefficient method and genetic algorithm are constructed to reduce the dimension of the query data. Thirdly, various neural networks are employed to model the relationship between unemployment rate data and query data. Fourthly, an optimal neural network is selected as the selective predictor by using the cross-validation method. Finally, the selective neural network predictor with the best feature subset is used to forecast unemployment trend. The empirical results show that the proposed method clearly outperforms the classical forecasting approaches for the unemployment rate prediction. These findings imply that data mining method, such as neural networks, together with web information, can be used as an alternative tool to forecast social/economic hotspot. © 2011 IEEE.

关键词： Forecasting

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Recovering accurate labeling information from partially valid data for effective multi-label learning 29

Recovering accurate labeling information from partially vali...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology China School of Computer Sci and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems

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Optimizing queries with expensive video predicates based on estimation of attribute cardinality

Optimizing queries with expensive video predicates based on ...

引用

International Conference on Multimedia Information Networking and Security

作者： Yu, Lisheng Zhang, Jianmei Wang, Shan Key Laboratory of Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769542584

With rapid advances in video processing technologies, video data increased rapidly and becomes popular in our daily life for both professional and consumer applications, e.g., surveillance, education, entertainment. Such needs require the data management system not only can store and access the video content, but also able to optimize the queries with expensive video predications in an effective and efficient way. In previous research literature, query optimizations in relational database systems (RDBMS) are often based on disk I/O cost of involved operations. However, for a query that contains expensive video predicates, traditional cost estimation model does not work well. Although researchers have proposed some approaches which can solve the problem in certain situations, there are still some unresolved issues, and it needs further optimization. In this paper, motivate from a real-world large supermarket's business data and video surveillance data management scenario, through considering the characteristics of video data and its expensive processing, we propose a novel query optimization approach that caches operators' results and reconstructs the join order based estimation for attribute cardinality. This approach reduces the invoking times of expensive video predicate in a greater degree and gives a better solution for mixed query optimization which contains traditional data types and large object operations. The experiment result is satisfactory while compare with existing expensive predicates query optimization methods. © 2010 IEEE.

关键词： Relational database systems

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Random rough subspace based neural network ensemble for insurance fraud detection

Random rough subspace based neural network ensemble for insu...

引用

International Workshop on Computational Transportation Science

作者： Xu, Wei Wang, Shengnan Zhang, Dailing Yang, Bo Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769543352

In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. © 2011 IEEE.

关键词： Insurance

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Random analysis of statistical rough set

Random analysis of statistical rough set

引用

2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Tsang, Eric C.C. Zhao, Su-Yun Faculty of Information Technology Macau University of Science and Technology C-Macau China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781509003891

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling. © 2016 IEEE.

关键词： Sampling

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Measuring semantic association in domain ontology

Measuring semantic association in domain ontology

引用

3rd International Conference on Semantics, knowledge, and Grid, SKG 2007

作者： Tian, Xuan Li, HaiHua Du, Xiaoyong School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872 China

ISBN: (纸本)0769530079

In domain ontology, Semantic Association(SA) is used to depict the correlation between two concepts. In this paper, we define Semantic Association Degree(SAD) for measuring SA in the domain ontology. We first present a method to measure SAD of two direct related concepts by evaluating the semantic relationship between them, and then give another method to measure SAD of two indirect related concepts though SAD of two directed neighboring concepts. A set of comparison experiments show the benefit of our approaches. © 2007 IEEE.

关键词： Semantics

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Detecting spam comments with malicious users'behavioral characteristics

Detecting spam comments with malicious users'behavioral char...

引用

2010 IEEE International Conference on Information Theory and Information Security, ICITIS 2010

作者： Wang, Qianqian Liang, Bin Shi, Wenchang Liang, Zhaohui Sun, Wei Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China Beijing Venustech Cybervision Co. Ltd Beijing 100094 China

ISBN: (纸本)9781424469406

In recent years, the spread of spam comments has become a main obstacle which limits the development of commercialized social networks. This paper analyzes the differences of behavioral characteristics between normal users and malicious users. Based on these characteristics, we propose several heuristic methods to detect spam comments. These methods evaluate comments from three perspectives, including time-frequency characteristic of comments, text similarity of comments and the number of target domains each user's comments refer to. In our collected dataset, our experimental results indicate the accuracy of our detection strategy I (strategy for high accuracy) and strategy II (strategy for wide coverage) are 100% and 92.6%, respectively. The preliminary evaluation of the proposed detection methods shows promising result. © 2010 IEEE.

关键词： Heuristic methods

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RETRACTED ARTICLE: Studies of knowledge management methods from literature

RETRACTED ARTICLE: Studies of knowledge management methods f...

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作者： Zhang, Jing Ye, Liuqi Business School of Hohai University Nanjing Jiangsu 210098 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing 100872 China

ISBN: (纸本)9781424469291

knowledge management methods reflect ways to undertake knowledge management objectives and actions taken for implementation. However, little has been written on the topic. The paper summarizes main topics of knowledge management methods from literature, including research contents, management methods, main types, objects involved, study domains, perspectives, objectives and tasks, school and achieving channels, developing trends and knowledge management methodology and etc. © 2010 IEEE.

关键词： knowledge management

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A support vector machine based method for credit risk assessment

A support vector machine based method for credit risk assess...

引用

International Workshop on Cloud Services-Platform Accelerating E-Business

作者： Xu, Wei Zhou, Shenghu Duan, Dongmei Chen, Yanhui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769542270

The credit card industry has been growing rapidly in recent years, and credit risk assessment becomes critically important for financial companies. In this paper, a novel support vector machine (SVM) based ensemble model is proposed for credit risk assessment. In the proposed method, principles component analysis (PCA) is firstly employed for credit feature selection. Secondly, SVMs with different kernels are trained by using genetic algorithm (GA) to optimize the parameters, and the corresponding assessment results are obtained. Thirdly, all results produced by different SVMs are combined by several ensemble strategies. Finally, an optimal ensemble strategy is selected for credit scoring. For validation, two real world credit datasets are used to test the effectiveness and efficiency of our proposed method. The experiment results find that our proposed ensemble model outperforms commonly used credit scoring tools. The findings of the study reveal the support vector machine based ensemble method to be a promising alternative for credit scoring. © 2010 IEEE.

关键词： Support vector machines

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Services analysis in apparel quotation system based on SOA

Services analysis in apparel quotation system based on SOA

引用

International Conference on Management and Service Science, MASS 2009

作者： Li, Qian Chen, Yu Lanfang, Zhang Information School and Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781424446391

Based on the analysis of quote business processes, as well as the characteristics of SOA, a quotation system is brought forward based on SOA for apparel international trade, in order to enhance the Quote's diversity, accuracy as well as efficiency. This paper puts great importance on analyzing how to define, extract, as well as implement services, not only for needs from quotation business process, but also from quotation business goals. Based on the analysis of services, finally, an apparel quotation architecture is described in details. ©2009 IEEE.

关键词： International trade

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