An Empirical Comparison of Machine and Deep Learning Algorithms’ Performance on Chemical Data
Pages 655 - 658
Abstract
Chemoinfomratics is a field which is concerned with modelling chemical data using computational methods. Over the years, machine learning algorithms such as support vector machine and random forest have gained popularity in modelling chemical classification problems. However, the rapid development of deep learning algorithms and explosion of chemical and biological data, have contributed to an extensive growth of deep learning studies. This paper presents an empirical comparison between some machine learning and deep learning algorithms on chemical data. The comparison is conducted based on multiple accuracy measures to investigate their learning ability. Our empirical results provide evidence of deep learning models superiority over machine learning models.
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November 2021
658 pages
ISBN:9781450395564
DOI:10.1145/3487664
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Published: 30 December 2021
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iiWAS2021
iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence
November 29 - December 1, 2021
Linz, Austria
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