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Experimental Evaluation of Artificial Immune System-Based Learning Algorithms

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Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 118))

Abstract

In this chapter, we present experimental results to test and compare the performance of Artificial Immune System-based clustering, classification and one-class classification algorithms. The test data are provided via an open access collection of 1000 pieces from 10 classes of western music. This collection has been extensively used in testing algorithms for music signal processing. Specifically, we perform extensive tests on:

  • Music Piece Clustering and Music Database Organization,

  • Customer Data Clustering in an e-Shopping Application,

  • Music Genre Classification, and

  • A Music Recommender System.

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Notes

  1. 1.

    STD stands for standard deviation.

  2. 2.

    GEN corresponds to the number of iterations performed.

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Authors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Dionisios N. Sotiropoulos & George A. Tsihrintzis

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  1. Dionisios N. Sotiropoulos
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  2. George A. Tsihrintzis
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Correspondence to Dionisios N. Sotiropoulos .

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Sotiropoulos, D.N., Tsihrintzis, G.A. (2017). Experimental Evaluation of Artificial Immune System-Based Learning Algorithms. In: Machine Learning Paradigms. Intelligent Systems Reference Library, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-319-47194-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-47194-5_8

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  • Print ISBN: 978-3-319-47192-1

  • Online ISBN: 978-3-319-47194-5

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