Final Remarks and Further Work for the Hybrid-AutoML System

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Copyright: 2021
Pages: 3
Source title: Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis
Source Author(s)/Editor(s): Zhongyu Lu (University of Huddersfield, UK), Qiang Xu (University of Huddersfield, UK), Murad Al-Rajab (University of Huddersfield, UK & Abu Dhabi University, UAE)and Lamogha Chiazor (University of Huddersfield, UK)
DOI: 10.4018/978-1-7998-7316-7.ch013

Keywords: Knowledge Management and Library Science / Machine Learning / Medical Diagnosis and Practice / Medical Information Science Reference

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Abstract

This chapter addresses that the various use cases have proved that the aims and contributions of this research to conceptualise, design, and develop a scalable and flexible toolkit for automatic big data ML mode and model selection, on single or multi-varying datasets has been achieved. A major benefit of the hybrid-autoML toolkit is that it reduces the time data scientists and researchers in the field spend, searching through the algorithm selections and hyper parameter space. This advantage was discussed in Section 5.2 where the authors compared the hybrid-autoML tool with autoWeka on about 35 datasets using measures such as accuracy, mean absolute error (MAE), and time.

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Final Remarks and Further Work for the Hybrid-AutoML System

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