Hybrid-AutoML System Development

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Copyright: 2021
Pages: 12
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.ch011

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

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Abstract

This chapter presents the Hybrid-AutoML system requirements, design materials, model algorithms, and model design, which encompasses the design goals, architecture (a three-layered architecture), components, and characteristics of the Hybrid-AutoML toolkit developed in this research for automatic mode and model selection on single or multi-varying datasets. The mode components, decision learning and AutoProbClass unsupervised algorithms, and application API are described. The testing and evaluation of the model is conducted by two case studies.

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