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Defining User Knowledge Level by Using Decision Tree Induction Approach

http://hdl.handle.net/20.500.12678/0000003510
e0eec996-dfb0-4656-9f10-ee95f2a02dc2
c9a7d64e-93fc-4d1e-b0d4-ba458bccfc05
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psc2010paper psc2010paper (50).pdf (328 Kb)
Publication type
Article
Upload type
Publication
Title
Title Defining User Knowledge Level by Using Decision Tree Induction Approach
Language en
Publication date 2010-12-16
Authors
Zin, Po Po
Aye, Hnin Hnin
Description
This paper describes the method to classify user’s knowledge level using decision tree induction. Decision trees can easily be converted to classification rules by using decision tree induction. This system is to estimate classifier accuracy that is important to evaluate how accurately a given classifier will label future data. In this paper, we present the classification of training data in which the resulting classifier is a decision tree induction. Decision Tree method for classification is exploited to identify user knowledge level after they proceed to learn lectures. As a result, user can know their knowledge level after learning and they can also test their knowledge level.
Keywords
Classification, data mining, decision tree, attribute, entropy
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1253
Journal articles
Fifth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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