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Using Result Profiles to Drive Meta-learning

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Information Systems (EMCIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 437))

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Abstract

Knowledge gained by meta-learning processes is valuable when it can be successfully used in solving algorithm selection problems. There is still strong need for automated tools for learning from data, performing model construction and selection with little or no effort from human operator. This article provides evidence for efficacy of a general meta-learning algorithm performing validations of candidate learning methods and driving the search for most attractive models on the basis of an analysis of learning results profiles. The profiles help in finding similar processes performed for other datasets and pointing to promising learning machines configurations. Further research on profile management is expected to bring very attractive automated tools for learning from data. Here, several components of the framework have been examined and an extended test performed to confirm the possibilities of the method. The discussion also touches on the subject of testing and comparing the results of meta-learning algorithms.

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Authors and Affiliations

  1. Institute of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, ul. Grudziądzka 5, 87-100, Toruń, Poland

    Krzysztof Grąbczewski

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  1. Krzysztof Grąbczewski
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Correspondence to Krzysztof Grąbczewski .

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Editors and Affiliations

  1. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

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Grąbczewski, K. (2022). Using Result Profiles to Drive Meta-learning. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-95947-0_6

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  • Online ISBN: 978-3-030-95947-0

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