Academic Journals Database
Disseminating quality controlled scientific knowledge

A Fuzzy Similarity Based Concept Mining Model for Text Classification

ADD TO MY LIST
 
Author(s): Shalini Puri

Journal: International Journal of Advanced Computer Sciences and Applications
ISSN 2156-5570

Volume: 2;
Issue: 11;
Start page: 115;
Date: 2011;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

Keywords: Text Classification | Natural Language Processing | Feature Extraction | Concept Mining | Fuzzy Similarity Analyzer | Dimensionality Reduction | Sentence Level | Document Level | Integrated Corpora Level Processing.

ABSTRACT
Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. In this paper, a new Fuzzy Similarity Based Concept Mining Model (FSCMM) is proposed to classify a set of text documents into pre - defined Category Groups (CG) by providing them training and preparing on the sentence, document and integrated corpora levels along with feature reduction, ambiguity removal on each level to achieve high system performance. Fuzzy Feature Category Similarity Analyzer (FFCSA) is used to analyze each extracted feature of Integrated Corpora Feature Vector (ICFV) with the corresponding categories or classes. This model uses Support Vector Machine Classifier (SVMC) to classify correctly the training data patterns into two groups; i. e., + 1 and – 1, thereby producing accurate and correct results. The proposed model works efficiently and effectively with great performance and high - accuracy results.
Save time & money - Smart Internet Solutions     

Tango Jona
Tangokurs Rapperswil-Jona