A novel classifier based on meaning for text classification

Abstract

Ganiz, Murat Can (Dogus Author) -- Akyokuş, Selim (Dogus Author) -- Conference full title: International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2015; Madrid; Spain; 2 September 2015 through 4 September 2015.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

Helmholtz principle based supervised and unsupervised feature selection methods for text mining

“…As a future work, we plan to use meaning score as metric for text classification. As our first attempt for text classification, a new classifier, called Supervised Meaning Classifier (SMC), is proposed in ( Ganiz, Tutkan, & Akyoku ¸s , 2015 ).…”