A Framework for Text Classification Using Evolutionary Contiguous Convolutional Neural Network and Swarm Based Deep Neural Network - Prabhakar, Sunil Kumar; Rajaguru, Harikumar; So, Kwangsub; Won, Dong-Ok (Frontiers in Computational Neuroscience, 2022)
To classify the texts accurately, many machine learning techniques have been utilized in the field of Natural Language Processing (NLP). For many pattern classification applications, a great success has been obtained when implemented with deep learning models rather than using ordinary machine learning techniques. Understanding the complex models and their respective relationships within the data determines the success of such deep learning techniques. But analyzing the suitable deep learning methods, techniques, architectures for text classification is a huge challenge for researchers. In this work, a Contiguous Convolutional Neural Network (CCNN) based on Differential Evolution (DE) is initially proposed and named as Evolutionary Contiguous Convolutional Neural Network (ECCNN) where the data instances of the input point are considered along with the contiguous data points in the dataset so that a deeper understanding is provided for the classification of the respective input, thereby boosting the performance of the deep learning model. Secondly, a swarm based Deep Neural Network (DNN) utilizing Particle Swarm Optimization (PSO) with DNN is proposed for the classification of text and it is named as Swarm DNN. This model is validated on two datasets and the best results are obtained when implemented with Swarm DNN model as it produced a high classification accuracy of 97.32% when tested on BBC newsgroup text dataset and 87.99% when tested on 20 newsgroup text dataset. Similarly, when implemented with ECCNN model, it produced a high classification accuracy of 97.11% when tested on BBC newsgroup text dataset and 88.76% when tested on 20 newsgroup text dataset.