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Multi-kernel Analysis Method for Intelligent Data Processing with Application to Prediction Making

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Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 309))

Abstract

One of the major issues in engineering is the development of systems that make accurate predictions. The advances in machine learning and data science have given rise to intelligent data processing that is used for developing smart engineering systems. In this paper, a new method is developed that makes use of multiple learning kernels to analyze a dataset to a set of patterns, and then select a subset of them to put them together and make predictions. The proposed framework utilizes a set of kernel modeled Gaussian processes where each one is equipped with a different kernel function. The proposed method is applied for prediction making on a set of electric load patterns and provides high accuracy as compared to single Gaussian process models.

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Authors and Affiliations

  1. University of Texas at San Antonio, San Antontio, TX, 78249, USA

    Miltiadis Alamaniotis

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  1. Miltiadis Alamaniotis
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Correspondence to Miltiadis Alamaniotis .

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Editors and Affiliations

  1. Gdynia Maritime University, Gydnia, Poland

    Ireneusz Czarnowski

  2. ‘Aurel Vlaicu’ University of Arad, Arad, Romania

    Robert J. Howlett

  3. KES International, Selby, UK

    Lakhmi C. Jain

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Alamaniotis, M. (2022). Multi-kernel Analysis Method for Intelligent Data Processing with Application to Prediction Making. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_25

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