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Machine learning as a service for enabling Internet of Things and People

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The future Internet is expected to connect billions of people, things and services having the potential to deliver a new set of applications by deriving new insights from the data generated from these diverse data sources. This highly interconnected global network brings new types of challenges in analysing and making sense of data. This is why machine learning is expected to be a crucial technology in the future, in making sense of data, in improving business and decision making, and in doing so, providing the potential to solve a wide range of problems in health care, telecommunications, urban computing, and others. Machine learning algorithms can learn how to perform certain tasks by generalizing examples from a range of sampling. This is a totally different paradigm than traditional programming language approaches, which are based on writing programs that process data to produce an output. However, choosing a suitable machine learning algorithm for a particular application requires a substantial amount of time and effort that is hard to undertake even with excellent research papers and textbooks. In order to reduce the time and effort, this paper introduces the TCDC (train, compare, decide, and change) approach, which can be thought as a ‘Machine Learning as a Service’ approach, to aid machine learning researchers and practitioners to choose the optimum machine learning model to use for achieving the best trade-off between accuracy and interpretability, computational complexity, and ease of implementation. The paper includes the results of testing and evaluating the recommenders based on the TCDC approach (in comparison with the traditional default approach) applied to 12 datasets that are available as open-source datasets drawn from diverse domains including health care, agriculture, aerodynamics and others. Our results indicate that the proposed approach selects the best model in terms of predictive accuracy in 62.5 % for regression tests performed and 75 % for classification tests.

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This work was partially supported by the EC project CogNet, 671625 (H2020-ICT-2014-2, Research and Innovation action) and in part supported by the Science Foundation Ireland ADAPT centre (Grant 13/RC/2106).

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Correspondence to Haytham Assem.

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Assem, H., Xu, L., Buda, T.S. et al. Machine learning as a service for enabling Internet of Things and People. Pers Ubiquit Comput 20, 899–914 (2016).

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