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
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"
Conference_Titel :
Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on
DOI :
10.1109/INISTA.2015.7276788