Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (8): 545-551.doi: 10.23940/ijpe.22.08.p2.545551

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RNN LSTM-based Deep Hybrid Learning Model for Text Classification using Machine Learning Variant XGBoost

Sandhya Alagarsamya,* and Visumathi Jamesb   

  1. aDepartment of Computer Science Engineering, Sathyabama Institute of Science and Technology, Chennai, 600100, India;
    bDepartment of Computer Science Engineering, Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, 600085, India
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address: sandhyalagar@gmail.com
  • About author:Sandhya Alagarsamy has completed the Bachelor’s degree in Computer Science and Engineering from SASTRA Deemed University, Master degree in Information Technology from Sathyabama University, Chennai, India. She is persuing her doctorate in the field of Big Data Analytics in Sathyabama University. She has 5 years of Industry experience and 7 years of teaching experience. She has published more than 5 papers in conferences and journals. Her current areas of interest include Bigdata, Artificial Intelligence and Deep learning.
    Dr. Visumathi James has completed the Bachelor’s degree in Computer Science and Engineering from Manonmaniam Sundaranar University, Master and Ph.D. degree in Computer Science and Engineering from Sathyabama University, Chennai, India. She is working as Professor in the department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India. She has 20 years of teaching experience. She has published more than 75 papers in conferences and journals. Her current areas of interest include Network Security, Data mining, Bigdata, Cloud Computing and Artificial Intelligence.

Abstract: Text classification is an emerging area in Natural Language Processing (NLP). On the other hand, traditional text classification methods need to be improved due to the complexity and semantic nature in text. In this paper, we build a hybrid deep learning model using Deep Learning (DL) and Machine learning (ML) models. This work combines two traditional neural networks namely Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) to extract the features from the text document. LSTM preserves the historical information for text sequences and extracts the features using the RNN structure. The extracted features are used to run on machine learning classification algorithms like AdaBoost and XGBoost to perform the final prediction. Thereby the proposed Deep Hybrid Model eliminates the fully connected classification layers from a typical Deep Learning model. The performance of proposed model is measured with other models and the results show that the deep hybrid model provides about 12% increased results in terms of accuracy in text classification.

Key words: deep learning, machine learning, text classification, hybrid model, RNN, LSTM