Comparison Of Naive Bayes And Support Vector Machine Classifiers On Document Classification
Zun Hlaing Moe, San Thida, Mie Mie Khin, Hlaing May Tin · 2018
The main objective is to classify the field from IT research papers and compare the accuracy in two classifiers. Classification is the form of data analysis that can be used to extract models describing important data class or to predict future data trends. The most important features are selected and data are prepared for learning and classification. Text classification is the process of assigning a document to one or more target categories based on its contents. Training and classification are performed using Naive Bayes and Support Vector Machine (SVM) classifiers. Experimental results show that the methods are favorable in terms of their effectiveness and efficiency. This system classifies text on ten categories such as “Big Data”, ”Image Processing”,” Data Mining”, “Artificial Intelligent”, “Ontology”, “Data Base Management System”, “Management Information System” and “Software Engineering” and so on. This system calculates the accuracy of testing data using holdout method.