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Randomizing Greedy Ensemble Outlier Detection with GRASP

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Complex, Intelligent, and Software Intensive Systems (CISIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 611))

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Abstract

Ensemble methods have been recently used in many applications of machine learning in different areas. In this context, outlier detection is an area where recently these methods have received increasing attention. This paper deals with randomization in ensemble methods for outlier detection. We have developed a novel algorithm exploiting stochastic local search heuristics to induce diversity in an ensemble outlier detection algorithm. We exploit the capability of the GRASP heuristic to induce diversity into the search process and to maintain a good balance of exploitation and diversification in building the ensemble. The conducted experiments show interesting improvements over the greedy ensemble method and open the path for novel research in this direction.

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Author information

Authors and Affiliations

  1. University of New York in Tirana, Kodra e Diellit, Tirana, Albania

    Lediona Nishani & Marenglen Biba

Authors
  1. Lediona Nishani
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  2. Marenglen Biba
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Correspondence to Lediona Nishani .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Politècnico di Torino, Istituto Superiore Mario Boella, Turin, Italy

    Olivier Terzo

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Nishani, L., Biba, M. (2018). Randomizing Greedy Ensemble Outlier Detection with GRASP. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_92

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  • DOI: https://doi.org/10.1007/978-3-319-61566-0_92

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61565-3

  • Online ISBN: 978-3-319-61566-0

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