International Symposium on Innovations in Intelligent SysTems and Applications (INISTA 2015), Madrid, İspanya, 2 - 04 Eylül 2015, ss.462-466
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 Naive Bayes ( MNB) and Support Vector Machine ( SVM) especially when the training data limited.