Prabhakar Sunil Kumar, Rajaguru Harikumar, So Kwangsub, Won Dong-Ok
Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea.
Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, India.
Front Comput Neurosci. 2022 Jun 29;16:900885. doi: 10.3389/fncom.2022.900885. eCollection 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, 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, and 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 Swarm DNN. This model is validated on two datasets and the best results are obtained when implemented with the Swarm DNN model as it produced a high classification accuracy of 97.32% when tested on the BBC newsgroup text dataset and 87.99% when tested on 20 newsgroup text datasets. Similarly, when implemented with the ECCNN model, it produced a high classification accuracy of 97.11% when tested on the BBC newsgroup text dataset and 88.76% when tested on 20 newsgroup text datasets.
为了准确地对文本进行分类，自然语言处理（NLP）领域已经采用了许多机器学习技术。对于许多模式分类应用而言，使用深度学习模型实现时比使用普通机器学习技术取得了更大的成功。理解数据中的复杂模型及其各自的关系决定了此类深度学习技术的成功。但是分析适用于文本分类的深度学习方法、技术和架构对研究人员来说是一项巨大的挑战。在这项工作中，最初提出了一种基于差分进化（DE）的连续卷积神经网络（CCNN），并将其命名为进化连续卷积神经网络（ECCNN），其中在数据集中考虑输入点的数据实例以及连续的数据点，以便为各个输入的分类提供更深入的理解，从而提高深度学习模型的性能。其次，提出了一种基于群体的深度神经网络（DNN），它将粒子群优化（PSO）与DNN相结合用于文本分类，并将其命名为群体DNN。该模型在两个数据集上进行了验证，当使用群体DNN模型实现时获得了最佳结果，因为在BBC新闻组文本数据集上测试时它产生了97.32%的高分类准确率，在20新闻组文本数据集上测试时为87.99%。同样，当使用ECCNN模型实现时，在BBC新闻组文本数据集上测试时它产生了97.11%的高分类准确率，在20新闻组文本数据集上测试时为88.76%。
