About: With the advent of big data, interest for new data mining methods has increased dramatically. The main drawback of traditional data mining methods is the lack of comprehensibility. In this paper, the firefly algorithm was employed for standalone binary classification, where each solution is represented by two classification rules that are easy understandable by users. Implicitly, the feature selection is also performed by the algorithm. The results of experiments, conducted on three well-known datasets publicly available on web, were comparable with the results of the traditional methods in terms of accuracy and, therefore, the huge potential was exhibited by the proposed method.   Goto Sponge  NotDistinct  Permalink

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  • With the advent of big data, interest for new data mining methods has increased dramatically. The main drawback of traditional data mining methods is the lack of comprehensibility. In this paper, the firefly algorithm was employed for standalone binary classification, where each solution is represented by two classification rules that are easy understandable by users. Implicitly, the feature selection is also performed by the algorithm. The results of experiments, conducted on three well-known datasets publicly available on web, were comparable with the results of the traditional methods in terms of accuracy and, therefore, the huge potential was exhibited by the proposed method.
Subject
  • Algorithms
  • Data management
  • Data mining
  • Model selection
  • Machine learning
  • Big data
  • Dimension reduction
  • Distributed computing problems
  • Statistical classification
  • Technology forecasting
  • Mathematical logic
  • Theoretical computer science
  • Formal sciences
  • Transaction processing
  • Nature-inspired metaheuristics
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