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Clustering and Regression to Impute Missing Values of Robot Performance

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

It is widely claimed that a major challenge in Robotics is to get reliable systems while both response and down times are minimized. In keeping with this idea, present paper proposes the application of a Hybrid Artificial Intelligence System (HAIS) to preprocess data with the aim of improving the detection of performance anomalies. One of the main problems when analyzing real-life data is the presence of missing values. It is usually solved by removing incomplete data, what causes a loss of information that may be critical in some domains. As an alternative, present paper proposes the application of regression models to impute those missing values. Prediction is optimized by generating personalized models on previously clustered data. Experiments are run on a public and up-to-date dataset that contains information about anomalies affecting the component-based software of a robot. The obtained results validate the proposed HAIS, as it successfully imputes missing values from the different features in the original dataset.

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Authors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Ángel Arroyo, Nuño Basurto, Carlos Cambra & Álvaro Herrero

Authors
  1. Ángel Arroyo
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  2. Nuño Basurto
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  3. Carlos Cambra
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  4. Álvaro Herrero
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Corresponding author

Correspondence to Ángel Arroyo .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Arroyo, Á., Basurto, N., Cambra, C., Herrero, Á. (2020). Clustering and Regression to Impute Missing Values of Robot Performance. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

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