A fuzzy approach to classification of text documents

WeiYi Liu; Ning Song

doi:10.1007/bf02947124

A fuzzy approach to classification of text documents

Journal of Computer Science and Technology ◽

10.1007/bf02947124 ◽

2003 ◽

Vol 18 (5) ◽

pp. 640-647 ◽

Cited By ~ 8

Author(s):

WeiYi Liu ◽

Ning Song

Keyword(s):

Fuzzy Approach ◽

Text Documents

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Computing Correlative Association of Terms for Automatic Classification of Text Documents

Proceedings of the Third International Symposium on Computer Vision and the Internet - VisionNet'16 ◽

10.1145/2983402.2983424 ◽

2016 ◽

Cited By ~ 3

Author(s):

Deepak Agnihotri ◽

Kesari Verma ◽

Priyanka Tripathi

Keyword(s):

Automatic Classification ◽

Text Documents

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Intuitionistic Fuzzy Approach to Enhance Text Documents

2006 3rd International IEEE Conference Intelligent Systems ◽

10.1109/is.2006.348511 ◽

2006 ◽

Author(s):

Jayanthi Kuppannan ◽

Parvathi Rangasamy ◽

Devi Thirupathi ◽

N. Palaniappan

Keyword(s):

Fuzzy Approach ◽

Text Documents ◽

Intuitionistic Fuzzy

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An efficient approach for dimensionality reduction and classification of high dimensional text documents

Proceedings of the First International Conference on Data Science, E-learning and Information Systems - DATA '18 ◽

10.1145/3279996.3281364 ◽

2018 ◽

Cited By ~ 2

Author(s):

Kotte Vinay Kumar ◽

R. Srinivasan ◽

E. B. Singh

Keyword(s):

Dimensionality Reduction ◽

High Dimensional ◽

Text Documents ◽

Efficient Approach

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Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents

Journal of Business and Economic Statistics ◽

10.1080/07350015.2014.903086 ◽

2014 ◽

Vol 32 (3) ◽

pp. 445-456 ◽

Cited By ~ 3

Author(s):

Guoyu Guan ◽

Jianhua Guo ◽

Hansheng Wang

Keyword(s):

Chinese Text ◽

Naive Bayes ◽

Naïve Bayes ◽

Text Documents

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Multifactorial fuzzy approach to the sawability classification of building stones

Construction and Building Materials ◽

10.1016/j.conbuildmat.2006.05.023 ◽

2007 ◽

Vol 21 (8) ◽

pp. 1672-1679 ◽

Cited By ~ 36

Author(s):

B. Tutmez ◽

S. Kahraman ◽

O. Gunaydin

Keyword(s):

Building Stones ◽

Fuzzy Approach

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Distributed Classification of Text Documents on Apache Spark Platform

Artificial Intelligence and Soft Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-39378-0_53 ◽

2016 ◽

pp. 621-630 ◽

Cited By ~ 5

Author(s):

Piotr Semberecki ◽

Henryk Maciejewski

Keyword(s):

Apache Spark ◽

Text Documents ◽

Distributed Classification

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Classification of Text Documents Using B-Tree

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Advances in Computer Science and Information Technology. Computer Science and Engineering ◽

10.1007/978-3-642-27308-7_66 ◽

2012 ◽

pp. 627-636

Author(s):

B. S. Harish ◽

D. S. Guru ◽

S. Manjunath

Keyword(s):

Text Documents

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Classification of Text Documents

Advances in Intelligent Systems and Computing - Advances in Artificial Intelligence and Data Engineering ◽

10.1007/978-981-15-3514-7_51 ◽

2020 ◽

pp. 675-685

Author(s):

Pushpa B. Patil ◽

Dakshayani M. Ijeri

Keyword(s):

Text Documents

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Multi-attribute Classification of Text Documents as a Tool for Ranking and Categorization of Educational Innovation Projects

Computational Linguistics and Intelligent Text Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-54903-8_34 ◽

2014 ◽

pp. 404-416

Author(s):

Alexey An ◽

Bakytkan Dauletbakov ◽

Eugene Levner

Keyword(s):

Educational Innovation ◽

Text Documents ◽

Innovation Projects ◽

Attribute Classification

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A Generic Framework for Feature Representations in Image Categorization Tasks

International Journal of Software Science and Computational Intelligence ◽

10.4018/jssci.2009062503 ◽

2009 ◽

Vol 1 (4) ◽

pp. 36-60

Author(s):

Adam Csapo ◽

Barna Resko ◽

Morten Lind ◽

Peter Baranyi

Keyword(s):

Visual Information ◽

Research Field ◽

Obstacle Detection ◽

Point Of View ◽

Visual Features ◽

Text Documents ◽

Feature Representations ◽

The Past ◽

Biological Perspective

The computerized modeling of cognitive visual information has been a research field of great interest in the past several decades. The research field is interesting not only from a biological perspective, but also from an engineering point of view when systems are developed that aim to achieve similar goals as biological cognitive systems. This article introduces a general framework for the extraction and systematic storage of low-level visual features. The applicability of the framework is investigated in both unstructured and highly structured environments. In a first experiment, a linear categorization algorithm originally developed for the classification of text documents is used to classify natural images taken from the Caltech 101 database. In a second experiment, the framework is used to provide an automatically guided vehicle with obstacle detection and auto-positioning functionalities in highly structured environments. Results demonstrate that the model is highly applicable in structured environments, and also shows promising results in certain cases when used in unstructured environments.

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