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Solve Classification Tasks with Probabilities. Statistically-Modeled Outputs

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Hybrid Artificial Intelligent Systems (HAIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10334))

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Abstract

In this paper, an approach for probability-based class prediction is presented. This approach is based on a combination of a newly proposed Histogram Probability (HP) method and any classification algorithm (in this paper results for combination with Extreme Learning Machines (ELM) and Support Vector Machines (SVM) are presented). Extreme Learning Machines is a method of training a single-hidden layer neural network. The paper contains detailed description and analysis of the HP method by the example of the Iris dataset. Eight datasets, four of which represent computer vision classification problem and are derived from Caltech-256 image database, are used to compare HP method with another probability-output classifier [11, 18].

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, The University of Iowa, Iowa City, USA

    Andrey Gritsenko & Amaury Lendasse

  2. Iowa Informatics Initiative, The University of Iowa, Iowa City, USA

    Andrey Gritsenko & Amaury Lendasse

  3. Lyrical Labs LLC, Iowa City, USA

    Andrey Gritsenko, Daniel Schupp & Edward Ratner

  4. Arcada University of Applied Sciences, Helsinki, Finland

    Emil Eirola & Amaury Lendasse

Authors
  1. Andrey Gritsenko
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  2. Emil Eirola
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  3. Daniel Schupp
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  4. Edward Ratner
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  5. Amaury Lendasse
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Correspondence to Andrey Gritsenko .

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Editors and Affiliations

  1. University of La Rioja , Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  2. University of La Rioja , Logroño, La Rioja, Spain

    Rubén Urraca

  3. University of A Coruña , Ferrol, La Coruña, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Gritsenko, A., Eirola, E., Schupp, D., Ratner, E., Lendasse, A. (2017). Solve Classification Tasks with Probabilities. Statistically-Modeled Outputs. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_25

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  • DOI: https://doi.org/10.1007/978-3-319-59650-1_25

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