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Defining User Knowledge Level by Using Decision Tree Induction Approach
http://hdl.handle.net/20.500.12678/0000003510
http://hdl.handle.net/20.500.12678/0000003510e0eec996-dfb0-4656-9f10-ee95f2a02dc2
c9a7d64e-93fc-4d1e-b0d4-ba458bccfc05
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psc2010paper (50).pdf (328 Kb)
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Article | ||||||
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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 |