2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献

Spatio-temporal event detection using probabilistic graphical models (PGMs) 基于概率图形模型的时空事件检测

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597221

Azadeh Mousavi, M. Duckham, K. Ramamohanarao, A. Rajabifard

{"title":"Spatio-temporal event detection using probabilistic graphical models (PGMs)","authors":"Azadeh Mousavi, M. Duckham, K. Ramamohanarao, A. Rajabifard","doi":"10.1109/CIDM.2013.6597221","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597221","url":null,"abstract":"Event detection concerns identifying occurrence of interesting events which are meaningful and understandable. In dynamic fields, as time passes the attribute of phenomenon varies in spatial locations. Detecting events in dynamic fields requires an approach to deal with the highly granular data arriving in real time. This paper proposes a spatiotemporal event detection algorithm in dynamic fields which are monitored by wireless sensor networks (WSNs). The algorithm provides a method using probabilistic graphical models (PGMs) in WSNs to cope with the uncertainty of sensor readings. The algorithm incorporates the ability of Markov chains in temporal dependency modelling and Markov random fields theory to model the spatial dependency of sensors in a distributed fashion. Experimental evaluation of the proposed algorithm demonstrates that the decentralized approach improves the F1-score to 82% and 29% better precision than simple threshold technique. In addition, the performance of the algorithm was evaluated and compared with respect to the scalability (in terms of communication complexity). In comparison with the centralized approach the decentralized algorithm can substantially improve the scalability of communication in wireless sensor networks.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129185572","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: 15

A Self-Organizing Time Map for time-to-event data 用于时间到事件数据的自组织时间图

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597241

Peter Sarlin

引用次数: 3

Automotive diagnosis typo correction using domain knowledge and machine learning 使用领域知识和机器学习的汽车诊断错字校正

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597246

Yinghao Huang, Y. Murphey, Yao Ge

引用次数: 18

Web product ranking using opinion mining 使用意见挖掘的网络产品排名

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597235

Yin-Fu Huang, Heng-Chang Lin

引用次数: 13

Class-dependent Canonical Correlation Analysis for scalable cross-lingual document categorization 可扩展跨语言文档分类的类相关典型相关分析

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597252

Mohamed Farouk Abdel Hady, M. Asham

{"title":"Class-dependent Canonical Correlation Analysis for scalable cross-lingual document categorization","authors":"Mohamed Farouk Abdel Hady, M. Asham","doi":"10.1109/CIDM.2013.6597252","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597252","url":null,"abstract":"Canonical Correlation Analysis (CCA) is used to infer a semantic space into which text documents, written in different languages, can be mapped to a language-independent representation, called latent topics. This highly reduces the complexity of dealing with different languages since we can train a document classifier using the labeled documents in one language, and then apply it to classify documents in another language. This topic modeling task is usually performed in a class-independent manner. The performance of CCA depends on the amount of documents used for inferring the semantic space. However, CCA has a high computational complexity with respect to the number of training documents. In this paper, we proposed a scalable variant of CCA, CD-CCA, to improve its scalability and complexity where the projection is performed in a class-dependent manner. It generates a semantic space for each category separately. Then a binary document classifier is trained for each category on its own semantic space. CD-CCA was applied on English-Chinese document classification. The experimental results showed that CD-CCA can deal with large training sets without hurting the performance of the underlying classifiers compared to traditional CCA. CD-CCA opens the door for distributed training of the semantic spaces of the different categories.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133730566","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

E-mail address categorization based on semantics of surnames 基于姓氏语义的电子邮件地址分类

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597240

Suresh Veluru, Y. Rahulamathavan, P. Viswanath, P. Longley, M. Rajarajan

{"title":"E-mail address categorization based on semantics of surnames","authors":"Suresh Veluru, Y. Rahulamathavan, P. Viswanath, P. Longley, M. Rajarajan","doi":"10.1109/CIDM.2013.6597240","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597240","url":null,"abstract":"Surname (family name) analysis is used in geography to understand population origins, migration, identity, social norms and cultural customs. Some of these are supposedly evolved over generations. Surnames exhibit good statistical properties that can be used to extract information in names data set such as automatic detection of ethnic or community groups in names. An e-mail address, often contains surname as a substring. This containment may be full or partial. An e-mail address categorization based on semantics of surnames is the objective of this paper. This is achieved in two phases. First phase deals with surname representation and clustering. Here, a vector space model is proposed where latent semantic analysis is performed. Clustering is done using the method called average-linkage method. In the second phase, an email is categorized as belonging to one of the categories (discovered in first phase). For this, substring matching is required, which is done in an efficient way by using suffix tree data structure. We perform experimental evaluation for the 500 most frequently occurring surnames in India and United Kingdom. Also, we categorize the e-mail addresses that have these surnames as substrings.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133707567","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: 3

A novel fuzzy classification to enhance software regression testing 一种新的模糊分类方法来增强软件回归测试

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597217

Zhiwei Xu, Yi Liu, Kehan Gao

{"title":"A novel fuzzy classification to enhance software regression testing","authors":"Zhiwei Xu, Yi Liu, Kehan Gao","doi":"10.1109/CIDM.2013.6597217","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597217","url":null,"abstract":"An effective system regression testing for consecutive releases of very large software systems, such as modern telecommunications systems, depends considerably on the selection of test cases for execution. Classification models can classify, early in the test planning phase, those test cases that are likely to detect faults in the upcoming regression test. Due to the high uncertainties in regression test, classification models based on fuzzy logic are very useful. Recently, methods have been proposed for automatically generating fuzzy if-then rules by applying complicated rule generation procedures to numerical data. In this research, we introduce and demonstrate a new rule-based fuzzy classification (RBFC) modeling approach as a method for identifying high effective test cases. The modeling approach, based on test case metrics and the proposed rule generation technique, is applied to extracting fuzzy rules from numerical data. In addition, it also provides a convenient way to modify rules according to the costs of different misclassification errors. We illustrate our modeling technique with a case study of large-scale industrial software systems and the results showed that test effectiveness and efficiency was significantly improved.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116311243","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: 4

Discovery of topological relations for spatial Activity Recognition 空间活动识别拓扑关系的发现

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597220

K. Bouchard, A. Bouzouane, B. Bouchard

引用次数: 12

Transaction-based link strength prediction in a social network 社交网络中基于交易的链接强度预测

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597236

Hassan Khosravi, Ali Bozorgkhan, O. Schulte

{"title":"Transaction-based link strength prediction in a social network","authors":"Hassan Khosravi, Ali Bozorgkhan, O. Schulte","doi":"10.1109/CIDM.2013.6597236","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597236","url":null,"abstract":"The revolution of social networks and methods of analyzing them have attracted interest in many research fields. Predicting whether a friendship holds in a social network between two individuals or not, link prediction, has been a heavily researched topic in the last decade . In this paper we investigate a related problem, link strength prediction: how to assign ratings or strengths to friendship links. A basic approach would be matrix factorization applied to only friendship ratings. However, the existence of extensive transactions among users may be used for better predictions. We propose a new type of multiple-matrix factorization model for incorporating a transaction matrix. We derive gradient descent update equations for learning latent factors that predict values in the target rating matrix. Multiple-matrix factorization can be seen as a data fusion technique, that combines evidence from different sources. In the social network application, the target matrix contains friendship ratings and the evidence matrices specify transaction intensities between users. To evaluate the model, we introduce data from Cloob, a popular Iranian social network as well as synthetic data.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"13 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860707","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: 8

Quantitative Measurements of model interpretability for the analysis of spectral data 光谱数据分析模式可解释性的定量测量

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597212

Andreas Backhaus, U. Seiffert

引用次数: 8