Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)最新文献_第2页

Theoretical and Experimental Study of a Meta-Typicalness Approach for Reliable Classification 可靠分类的元典型性方法的理论与实验研究

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.158

E. Smirnov, A. Kaptein

引用次数: 13

Cascaded Data Mining Methods for Text Understanding, with medical case study 用于文本理解的级联数据挖掘方法，包括医学案例研究

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.38

Roni Romano, L. Rokach, O. Maimon

{"title":"Cascaded Data Mining Methods for Text Understanding, with medical case study","authors":"Roni Romano, L. Rokach, O. Maimon","doi":"10.1109/ICDMW.2006.38","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.38","url":null,"abstract":"Substantial electronically stored textual data such as clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. Hence, when searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved were irrelevant. We present a new cascaded pattern learning method for automatic identification of negative context in clinical narratives reports. Studying the training corpuses, the classification errors and patterns selected by the classifier, we noticed that it is possible to create a more powerful ensemble structure than the structure obtained from general-purpose ensemble method (such as Adaboost). We compare the new algorithm to previous methods proposed for the same task of similar medical narratives, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"61 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114003791","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 Hybrid Strategy for Clustering Data Mining Documents 数据挖掘文档聚类的混合策略

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.6

Yi Peng, Gang Kou, Yong Shi, Zhengxin Chen

引用次数: 6

Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features 具有超大空间特征的多元时间序列特征子集选择

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.81

Hyunjin Yoon, C. Shahabi

{"title":"Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features","authors":"Hyunjin Yoon, C. Shahabi","doi":"10.1109/ICDMW.2006.81","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.81","url":null,"abstract":"Several spatio-temporal data collected in many applications, such as fMRI data in medical applications, can be represented as a multivariate time series (MTS) matrix with m rows (capturing the spatial features) and n columns (capturing the temporal observations). Any data mining task such as clustering or classification on MTS datasets are usually hindered by the large size (i.e., dimensions) of these MTS items. In order to reduce the dimensions without losing the useful discriminative features of the dataset, feature selection techniques are usually preferred by domain experts since the relation of the selected subset of features to the originally acquired features is maintained. In this paper, we propose a new feature selection technique for MTS datasets where their spatial features (i.e., number of rows) are much larger than their temporal observations (i.e., number of columns), or m Gt n. Our approach is based on principal component analysis, recursive feature elimination and support vector machines. Our empirical results on real-world datasets show that our technique significantly outperforms the closest competitor technique","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128002695","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: 21

Asset Valuation Technique for Network Management and Security 网络管理与安全中的资产评估技术

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.32

L. Beaudoin, P. Eng

引用次数: 15

Mining and Predicting Duplication over Peer-to-Peer Query Streams 点对点查询流的重复挖掘和预测

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.109

S. Meng, Yifeng Shao, Congcong Shi, Dingyi Han, Yong Yu

引用次数: 1

Discovering Frequent Poly-Regions in DNA Sequences 发现DNA序列中的频繁多聚区

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.63

P. Papapetrou, Gary Benson, G. Kollios

引用次数: 13

The Role of Domain Ontology in Text Mining Applications: The ADDMiner Project 领域本体在文本挖掘应用中的作用:ADDMiner项目

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.157

A. C. Garcia, I. N. Ferraz, F. B. Pinto

引用次数: 8

An Evaluation of Progressive Sampling for Imbalanced Data Sets 不平衡数据集渐进式抽样的评价

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.28

Willie Ng, M. Dash

引用次数: 30

Mining Closed and Maximal Frequent Induced Free Subtrees 挖掘闭合和最大频繁诱导自由子树

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.111

Hitohiro Shiozaki, Tomonobu Ozaki, T. Ohkawa

引用次数: 3