Comparison between Record to Record Travel and Great Deluge Attribute Reduction Algorithms for Classification Problem

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 378)


In this paper, two single-solution-based meta-heuristic methods for attribute reduction are presented. The first one is based on a record-to-record travel algorithm, while the second is based on a Great Deluge algorithm. These two methods are coded as RRT and m-GD, respectively. Both algorithms are deterministic optimisation algorithms, where their structures are inspired by and resemble the Simulated Annealing algorithm, while they differ in the acceptance of worse solutions. Moreover, they belong to the same family of meta-heuristic algorithms that try to avoid stacking in the local optima by accepting non-improving neighbours. The obtained reducts from both algorithms were passed to ROSETTA and the classification accuracy and the number of generated rules are reported. Computational experiments confirm that RRT m-GD is able to select the most informative attributes which leads to a higher classification accuracy.


Record to Record Travel algorithm Great Deluge algorithm Rough Set Theory Classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Data Mining and Optimization Research Group (DMO), Center for Artificial Intelligence TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Computer Science, Faculty of Information TechnologyBirzeit UniversityBirzeitPalestine

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