Title: Suitability and importance of deep learning feature space in the domain of text categorisation
Authors: Rajendra Kumar Roul
Addresses: Thapar Institute of Engineering and Technology, Patiala, Punjab-147004, India
Abstract: One of the important features of multilayer ELM (ML-ELM) is its capability of nonlinearly mapping the features to an extended dimensional space and thereby builds the input features linearly separable. This paper studies the significance of deep learning feature space using ML-ELM for classification of text data which is the follow-up of my earlier approach. The previous approach discusses a new feature selection technique named combined cohesion separation and silhouette coefficient (CCSS) to generate a good feature vector and then used it for classification of text data using ML-ELM, which is a deep learning classifier. That approach is extended here which has two main aspects. The first aspect is to compare the performance of CCSS approach with the traditional feature selection techniques and the second aspect is to test the performance of different conventional classification techniques on the higher dimensional feature space of ML-ELM. Results of the experiment on different benchmark datasets justify that the proposed CCSS technique is comparable with the existing feature selection techniques and the ML-ELM feature space is more promising compared to the traditional TF-IDF vector space for classification of text data.
Keywords: classification; cohesion; deep learning; extreme learning machine; multilayer ELM; silhouette coefficient.
International Journal of Computational Intelligence Studies, 2019 Vol.8 No.1/2, pp.73 - 102
Received: 07 Mar 2018
Accepted: 15 Jul 2018
Published online: 14 Feb 2019 *