18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)最新文献_第3页

Text categorization using the semi-supervised fuzzy c-means algorithm 基于半监督模糊c均值算法的文本分类

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781756

M. Benkhalifa

{"title":"Text categorization using the semi-supervised fuzzy c-means algorithm","authors":"M. Benkhalifa","doi":"10.1109/NAFIPS.1999.781756","DOIUrl":"https://doi.org/10.1109/NAFIPS.1999.781756","url":null,"abstract":"Text categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has become very important in the information retrieval area, where information needs have tremendously increased with the rapid growth of textual information sources such as the Internet. We compare, for text categorization, two partially supervised (or semi-supervised) clustering algorithms: the Semi-Supervised Agglomerative Hierarchical Clustering (ssAHC) algorithm (A. Amar et al., 1997) and the Semi-Supervised Fuzzy-c-Means (ssFCM) algorithm (M. Amine et al., 1996). This (semi-supervised) learning paradigm falls somewhere between the fully supervised and the fully unsupervised learning schemes, in the sense that it exploits both class information contained in labeled data (training documents) and structure information possessed by unlabeled data (test documents) in order to produce better partitions for test documents. Our experiments, make use of the Reuters 21578 database of documents and consist of a binary classification for each of the ten most populous categories of the Reuters database. To convert the documents into vector form, we experiment with different numbers of features, which we select, based on an information gain criterion. We verify experimentally that ssFCM both outperforms and takes less time than the Fuzzy-c-Means (FCM) algorithm. With a smaller number of features, ssFCM's performance is also superior to that of ssAHC's. Finally ssFCM results in improved performance and faster execution time as more weight is given to training documents.","PeriodicalId":335957,"journal":{"name":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115425983","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: 41

An efficient implementation to calculate relative core and reducts 一种计算相对核和约简的有效实现

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781802

R. Bautista, M. Millán, J.F. Diaz

引用次数: 3

Optimal function approximation using fuzzy rules 使用模糊规则的最优函数逼近

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781679

D. Lisin, M. Gennert

引用次数: 9

A cognitive model for context dependent fuzzy knowledge 上下文相关模糊知识的认知模型

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781732

K. Eksioglu, G. Lachiver

{"title":"A cognitive model for context dependent fuzzy knowledge","authors":"K. Eksioglu, G. Lachiver","doi":"10.1109/NAFIPS.1999.781732","DOIUrl":"https://doi.org/10.1109/NAFIPS.1999.781732","url":null,"abstract":"Context dependency is an important problem of knowledge. Fuzzy information also suffers from this dependency when any fuzzy concept, once described, is isolated from its context. The paper describes a cognitive model in which fuzzy knowledge is processed with respect to its surrounding context. The model is derived from some psycho-cognitive approaches: the integrity of knowledge and its context as well as the episodic character of contextual effects on knowledge. The model consists of some functional modules performing related cognitive tasks. Fuzzy knowledge is kept within a layered long-term memory. Layers form an episodic base for contextual knowledge: each layer represents a different context and keeps related fuzzy knowledge. A context selector module evaluates existence of context information in order to choose appropriate context layers. An aggregation module which is an optimization module evaluates implicit contexts. The model is supported by a car brake example, which shows its simplicity and adaptability, and thus the power of this cognitive model. The model is useful especially for mobile fuzzy system applications where knowledge on environment conditions are constantly changing and adaptation of the fuzzy system's descriptive aspects are necessary.","PeriodicalId":335957,"journal":{"name":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124835363","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

Control of granulation process by fuzzy logic 用模糊逻辑控制制粒过程

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781825

S. Watano, K. Miyanami

引用次数: 5

Learning to identify fuzzy regions in magnetic resonance images 学习识别磁共振图像中的模糊区域

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781713

S.E. Crane, L. Hall

引用次数: 2

Knowledge discovery using Cartesian granule features with applications 利用笛卡尔粒子特征的知识发现与应用

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781688

J. Shanahan, J. Baldwin, T. Martin

引用次数: 0

A fuzzy logic based expert system for short term energy negotiations 基于模糊逻辑的短期能源谈判专家系统

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781672

P. Gustavo

引用次数: 3

Matching attributes in a fuzzy case based reasoning 基于模糊案例推理的属性匹配

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781647

G. Dvir, G. Langholz, Moti Schneider

引用次数: 30

FULSOME: a fuzzy logic modeling tool for software metricians FULSOME:软件度量师的模糊逻辑建模工具

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI: 10.1109/NAFIPS.1999.781695

Stephen G. MacDonell, A. Gray, J. M. Calvert

引用次数: 21