Text Classification Performance Analysis on Machine Learning - P Praveen, R Ravi Kumar, M Babu Reddy (International Journal of Advanced Science and Technology, 2019)
Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. In general, text classification plays an important role in information extraction and summarization, text retrieval, and question answering. Intrusion detection system plays an important role in network security. Intrusion detection model is a predictive model used topredict the network data traffic as normal or intrusion. Machine Learning algorithms are used to build accurate models for clustering, classification and prediction. Labeled text documents are used to classify the text in supervised classifications. This paper applied these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analysis the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.