2017 IEEE International Conference on Information Reuse and Integration (IRI)最新文献_第4页

Upgrade of Highly Available Systems: Formal Methods at the Rescue 高可用性系统的升级:救援中的正式方法

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.66

Oussama Jebbar, F. Khendek, M. Toeroe

引用次数: 1

Visualization Analysis of Medical Resource Utilization for Long-Term Care Services 长期照护服务医疗资源利用可视化分析

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.78

Kuo-Chung Chu, Peng-Hua Jiang

引用次数: 1

An Effective LmRMR for Financial Variable Selection and its Applications 金融变量选择的有效LmRMR及其应用

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.35

Sara Aghakhani, R. Alhajj, J. Rokne, Philip Chang

{"title":"An Effective LmRMR for Financial Variable Selection and its Applications","authors":"Sara Aghakhani, R. Alhajj, J. Rokne, Philip Chang","doi":"10.1109/IRI.2017.35","DOIUrl":"https://doi.org/10.1109/IRI.2017.35","url":null,"abstract":"Financial variables are of primary importance in financial modeling, fraud detection, financial distress management, price modeling, credit and risk evaluations and in evaluating the return on assets and portfolios. There usually exist a large number of financial variables, where their exhaustive integration in a model increases its dimensionality and the associated computational time. We extensively tackle this problem in this paper. In this paper, we present a modified version of mRMR feature selection model to deal with financial features by ranking features first and then finding the best subset and uncertainty related to it using likelihood evaluation. The wellknown measurement formula of mRMR is considered for ranking financial features using correlation similarity measurement and the concept of minimum redundancy and maximum relevance of financial features and return of assets. Then, likelihood calculations inherently account for the mutual correlations between the variables as well as between the variables and the return on asset and result in a unique ‘likelihood’ value that has a correlation with the return on asset that can be maximized by adding and removing variables from the subset. We conducted experimental studies on Dow Jones Industrial Average to study the effectiveness and applicability of the proposed approach both in terms of financial variable selection as well as its application in Stock trading recommendation model and potential price forecasting. The performance is evaluated and the proposed approach shows promise.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122675762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Novel Approach to Neural Network Based Intelligent Systems for Emotion Recognition in Audio Signals 基于神经网络的音频信号情感识别智能系统的新方法

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.95

A. Mooman, Stephen Powers

引用次数: 0

Influence Analysis in Online Social Networks Using Hypergraphs 利用超图分析在线社交网络的影响

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.72

F. Amato, Francesco di Lillo, V. Moscato, A. Picariello, Giancarlo Sperlí

引用次数: 1

Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims 检测异常医疗保险索赔的医疗提供者专业预测

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.29

Matthew Herland, Richard A. Bauder, T. Khoshgoftaar

{"title":"Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims","authors":"Matthew Herland, Richard A. Bauder, T. Khoshgoftaar","doi":"10.1109/IRI.2017.29","DOIUrl":"https://doi.org/10.1109/IRI.2017.29","url":null,"abstract":"Fraud, waste, and abuse in medical insurance contributes to significant increases in costs for providers and patients. One way to reduce costs is through the detection of abnormal medical practices that could indicate possible fraud. In this paper, we expand upon our previous research into medical specialty anomaly detection by validating the efficacy of our model using real-world fraud cases, and then testing three strategies to improve model performance. The three strategies are feature selection (to include adjusting for class imbalance), medical specialty grouping, and the removal of specific, overlapping specialties. We use the publicly available Medicare claims data, released by the Center for Medicare and Medicaid Services, for building and testing our models. In addition to using the 2013 data, we use the 2014 data for model validation and comparisons. We employ the List of Excluded Individuals and Entities (LEIE) database, released by the Office of Inspector General, as well as two other documented fraud cases, for model testing. Multinomial Naïve Bayes is used to build all models. In this work, we confirm our prior model was able to correctly classify 67% of the real-world fraudulent physicians contained in the LEIE database as fraudulent. Furthermore, the three proposed strategies show good results in improving model performance.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132278160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Schemaless Join for Result Set Preferences 结果集首选项的无模式连接

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.26

Chuancong Gao, J. Pei, Jiannan Wang, Yi Chang

{"title":"Schemaless Join for Result Set Preferences","authors":"Chuancong Gao, J. Pei, Jiannan Wang, Yi Chang","doi":"10.1109/IRI.2017.26","DOIUrl":"https://doi.org/10.1109/IRI.2017.26","url":null,"abstract":"In many applications, such as data integration and big data analytics, one has to integrate data from multiple sources without detailed and accurate schema information. The state of the art focuses on matching attributes among sources based on the information derived from the data in those sources. However, a best join result according to a method's own pre-determined criteria may not fit a user's best interest. In this paper, we tackle the challenge from a novel angle and investigate how to join schemaless tables to meet a user preference the best. We identify a set of essential preferences that are useful in various scenarios, such as minimizing the number of tuples in outer join results and maximizing the entropy of the joining key's distribution. We also develop a systematic method to compute the best join predicate optimizing an objective function representing a user preference. We conduct extensive experiments on 4 large datasets and compare with 4 baselines from the state of the art of schema matching and attribute clustering. The experimental results clearly show that our algorithm outperforms the baselines significantly in accuracy in all the cases, and consumes comparable running time.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133613180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Automated Approach for Information and Referral of Social Services Using Machine Learning 使用机器学习的社会服务信息和转介的自动化方法

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.42

M. Sharan, N. K. Ottilingam, C. Mattmann, Karanjeet Singh, M. Marin, Amy Latzer, Colin Foon, Umesh Handore

{"title":"An Automated Approach for Information and Referral of Social Services Using Machine Learning","authors":"M. Sharan, N. K. Ottilingam, C. Mattmann, Karanjeet Singh, M. Marin, Amy Latzer, Colin Foon, Umesh Handore","doi":"10.1109/IRI.2017.42","DOIUrl":"https://doi.org/10.1109/IRI.2017.42","url":null,"abstract":"The Information and Referral Federation of Los Angeles County (211 LA County) is a nationally recognized service center that makes referrals to those in need of social service resources available at sites throughout Los Angeles County and nationally for those in need and for at-risk populations. Referrals are currently made using an on-line web-based referral system backed by a rich highly curated dataset collected over years and informed by a national taxonomy of social services. In support of resource referrals both for our on-line system, and for a new website presence, our research team has investigated and realized an automated resource referral system that learns from a caller's demographic information and historical referral data collected by human experts to recommend sites at the time of an active call. This system leverages a state of art multi-label neural network classifier, tuned by grid search for obtaining the best hyper-parameters for this system. The automated approach we have created allows 211 LA County to interactively provide a meaningful referral to those in need. In this paper, we describe our evaluation strategy and accuracy of our system on a one-year dataset containing over 450 thousand calls.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134109614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Constructing Hierarchical Product Categories for E-Commerce by Word Embedding and Clustering 基于词嵌入和聚类的电子商务分层产品类别构建

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.81

Yi-Hsiang Hsieh, Shih-Hung Wu, Liang-Pu Chen, Ping-Che Yang

引用次数: 4

Automated Dynamic Negotiation over Environmental Issues 环境问题的自动动态协商

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.34

F. Eshragh, M. Shahbazi, B. Far

{"title":"Automated Dynamic Negotiation over Environmental Issues","authors":"F. Eshragh, M. Shahbazi, B. Far","doi":"10.1109/IRI.2017.34","DOIUrl":"https://doi.org/10.1109/IRI.2017.34","url":null,"abstract":"Negotiation is a common means of resolving conflicts in social interactions. A popular approach for modeling social negotiation is automated negotiation. It is a distributed search in the space of potential agreements, facilitated by an agent-based model (ABM). Although automated negotiation is extensively applied in different fields of e-commerce, its application in environmental studies is still unexplored. This paper aims to lead the negotiation process over environmental issues in an efficient way where the possible agreement can be reached in few rounds of negotiation. To achieve this goal, an ABM is developed which has two significant characteristics. First, the proposer agent automatically learns the preferences of all stakeholder using the arguments and responses received from them in the rounds of negotiation. Second, the proposer accelerates the negotiation by automating the process of proposal-offering. To this end, first, the problem of proposal selection in one-to-one negotiation with each stakeholder is modeled using Markov Random Fields (MRF) and is solved using a belief propagation-based approach. Then, the proposer applies statistical analysis to identify the most optimal proposal and conducts a one-to-many negotiation.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129068424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1