Capsule Network Algorithm for Performance Optimization of Text Classification
Samuel Manoharan J
Abstract
In regions of visual inference, optimized performance is demonstrated by capsule networks on structured data. Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper. It is further compared to support vector machine (SVM), Long Short Term Memory (LSTM), artificial neural network (ANN), convolutional Neural Network (CNN) and other neural and non-neural network architectures to demonstrate its superior performance. The Blurb Genre Collection (BGC) and Web of Science (WOS) datasets are used for experimental purpose. The encoded latent data is combined with the algorithm while handling structurally diverse categories and rare events in hierarchical multi-label text applications.