Authors :
Leena Bhuskute; Satish kumar Varma
Volume/Issue :
Volume 5 - 2020, Issue 7 - July
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2ElwJxw
DOI :
10.38124/IJISRT20JUL380
Abstract :
Now a day there is rapid growth of the
World Wide Web. The work of instinctive classification
of documents is a main process for organizing the
information and knowledge discovery. The classification
of e-documents, online bulletin, blogs, e-mails and
digital libraries need text mining, machine learning and
natural language processing procedures to develop
significant information. Therefore, proper classification
and information detection from these assets is a
fundamental field for study. Text classification is
significant study topic in the area of text mining, where
the documents are classified with supervised
information. In this paper various text representation
schemes and learning classifiers such as Naïve Bayes
and Decision Tree algorithms are described with
illustration for predefined classes. This present
approaches are compared and distinguished based on
quality assurance parameters.
Keywords :
Classifiers, Machine learning, Supervised learning, Text classification.
Now a day there is rapid growth of the
World Wide Web. The work of instinctive classification
of documents is a main process for organizing the
information and knowledge discovery. The classification
of e-documents, online bulletin, blogs, e-mails and
digital libraries need text mining, machine learning and
natural language processing procedures to develop
significant information. Therefore, proper classification
and information detection from these assets is a
fundamental field for study. Text classification is
significant study topic in the area of text mining, where
the documents are classified with supervised
information. In this paper various text representation
schemes and learning classifiers such as Naïve Bayes
and Decision Tree algorithms are described with
illustration for predefined classes. This present
approaches are compared and distinguished based on
quality assurance parameters.
Keywords :
Classifiers, Machine learning, Supervised learning, Text classification.