2007 IEEE Symposium on Computational Intelligence and Data Mining最新文献_第10页

Privacy Preserving Burst Detection of Distributed Time Series Data Using Linear Transforms 基于线性变换的分布式时间序列数据隐私保护突发检测

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368937

L. Singh, Mehmet Sayal

引用次数: 8

Knowledge Based Stacking of Hyperspectral Data for Land Cover Classification 基于知识的土地覆盖分类高光谱数据叠加

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368890

Yangchi Chen, M. Crawford, Joydeep Ghosh

{"title":"Knowledge Based Stacking of Hyperspectral Data for Land Cover Classification","authors":"Yangchi Chen, M. Crawford, Joydeep Ghosh","doi":"10.1109/CIDM.2007.368890","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368890","url":null,"abstract":"Hyperspectral data provide new capability for discriminating spectrally similar classes, but unfortunately such class signatures often overlap in multiple narrow bands. Thus, it is useful to incorporate reliable spatial information when possible. However, this can result in increased dimensionality of the feature vector, which is already large for hyperspectral data. Markov random field (MRF) approaches, such as iterated conditional modes (ICM), can provide evidence relative to the class of a neighbor through Gibbs' distribution, but suffer from computational requirements and curse of dimensionality issues when applied to hyperspectral data. In this paper, a new knowledge based stacking approach is presented to utilize spatial information within homogeneous regions and at class boundaries, while avoiding the curse of dimensionality. The approach learns the location of the class boundary and combines original bands with the extracted spectral information of a neighborhood to train a hierarchical support vector machine (HSVM) classifier. The new method is applied to hyperspectral data collected by the Hyperion sensor on the EO-1 satellite over the Okavango delta of Botswana. Classification accuracies are compared to those obtained by a pixel-wise HSVM classifier, majority filtering and ICM to demonstrate the advantage of the knowledge based stacking approach.","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134355420","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

Fuzzy Wavelet Modeling Using Data Clustering 基于数据聚类的模糊小波建模

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368861

N. Sadati, B. Marami

引用次数: 2

Navigation Space Based Intranet Usability Analysis 基于导航空间的Intranet可用性分析

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368866

P. Géczy, Noriaki Izumi, S. Akaho, K. Hasida

引用次数: 2

An Efficient Data Management Scheme Based on Spatial and Temporal Characteristics in Virtual Environments 一种基于虚拟环境时空特征的高效数据管理方案

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368883

Hsing-Jen Chen, D. Liu

引用次数: 0

Analysis of streaming GPS measurements of surface displacement through a web services environment 通过web服务环境分析流GPS测量的地表位移

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368951

R. Granat, G. Aydin, M. Pierce, Zhigang Qi, Y. Bock

引用次数: 14

Collaborative Knowledge Discovery & Data Mining: From Knowledge to Experience 协同知识发现与数据挖掘:从知识到经验

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368905

T. Horeis, B. Sick

{"title":"Collaborative Knowledge Discovery & Data Mining: From Knowledge to Experience","authors":"T. Horeis, B. Sick","doi":"10.1109/CIDM.2007.368905","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368905","url":null,"abstract":"Experts have important qualitative knowledge about interrelations between more or less abstract concepts in an application area. However, the knowledge of a single expert is typically quite uncertain (e.g., incomplete or imprecise). By fusing the knowledge of several experts it would be possible to obtain more certain and, therefore, more valuable knowledge. Conventional systems for knowledge discovery (KD) and data mining (DM) have the ability to extract valid rules from huge data sets. These rules describe dependencies between attributes and classes in a quantitative way, for instance. By fusing this kind of knowledge with the combined, qualitative knowledge of several experts it would be possible to obtain more comprehensive knowledge about an application area. In this article, we propose a concept for a new KD & DM technique based on computational intelligence: collaborative knowledge discovery (CKD). These techniques combines the uncertain knowledge of several experts using methods based on Dempster-Shafer theory. The combined human knowledge is again fused with automatically extracted, well interpretable knowledge (fuzzy rules embedded in a radial basis function neural network) of a conventional KD system. Thus, a CKD system not only acquires more comprehensive knowledge, but also experience (knowledge about knowledge), meaning that it is able to explain automatically extracted rules to the human experts and to assess the interestingness (e.g., novelty or utility) of these rules. This can be done by adapting inference mechanisms from the field of probabilistic argumentation systems. A CKD system will comprise self-awareness mechanisms (it must know what it knows) as well as environment-awareness mechanisms (it must know what human experts know or what they want to now). In order to reduce the effort for knowledge acquisition, a CKD system must learn (pro-)actively. There are many application areas for such CKD systems, e.g., in the field of technical data mining (quality control, process monitoring, etc.)","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134449089","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: 26

Induction Tree methods to classify M. tuberculosis spoligotypes 诱导树法分类结核分枝杆菌孢子型

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368859

Georges Valétudie

引用次数: 0

Selective Approach To Handling Topic Oriented Tasks On The World Wide Web 在万维网上处理主题导向任务的选择性方法

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368894

Amit Awekar, Jaewoo Kang

引用次数: 5

A Dynamic Graph Model for Analyzing Streaming News Documents 流新闻文档分析的动态图模型

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368911

E. Hohman, D. Marchette

引用次数: 1