Abstract
Text data available on the Web are generally unstructured. Text classification, a machine learning technique, has proven to be a great alternative to structure textual data in a cost-effective, faster, and scalable manner. This study examines the feature space of Multilayer ELM (ML-ELM) for the classification of text data with the help of a novel feature selection technique termed as Correlation-based Feature Selection (CRFS). Experimental results show that the feature space of ML-ELM is better for text classification compared to the traditional vector space.
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We thank Thapar Institute of Engineering and Technology for providing the seed money grant to do this research work.
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Roul, R.K., Sahoo, J.K., Satyanath, G. (2023). Text Classification Using Correlation Based Feature Selection on Multi-layer ELM Feature Space. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_27
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DOI: https://doi.org/10.1007/978-3-031-24848-1_27
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