Randomizing Greedy Ensemble Outlier Detection with GRASP

  • Lediona NishaniEmail author
  • Marenglen Biba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 611)


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.


Outlier detection Ensemble methods Machine learning Stochastic local search GRASP 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of New York in TiranaTiranaAlbania

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