DocumentCode :
3659885
Title :
A novel classifier based on meaning for text classification
Author :
Murat Can Ganiz;Melike Tutkan;Selim Akyokuş
Author_Institution :
Computer Engineering Department of Doğ
fYear :
2015
Firstpage :
1
Lastpage :
5
Abstract :
Text classification is one of the key methods used in text mining. Generally, traditional classification algorithms from machine learning field are used in text classification. These algorithms are primarily designed for structured data. In this paper, we propose a new classifier for textual data, called Supervised Meaning Classifier (SMC). The new SMC classifier uses meaning measure, which is based on Helmholtz principle from Gestalt Theory. In SMC, meaningfulness of terms in the context of classes are calculated and used for classification of a document. Experiment results show that new SMC classifier outperforms traditional classifiers of Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM) especially when the training data limited.
Keywords :
"Training","Support vector machines","Text categorization","Accuracy","Classification algorithms","Context"
Publisher :
ieee
Conference_Titel :
Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on
Type :
conf
DOI :
10.1109/INISTA.2015.7276788
Filename :
7276788
Link To Document :
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