GAssist is a Pittsburgh-style learning classifier system (LCS). It uses a standard genetic algorithm to evolve a population of individuals, each of them being a complete and variable-length rule set. This system incorporates several mechanisms to tackle data mining problems: A windowing system Incremental Learning with Alternative Strata (ILAS) to improve its efficiency, a representation for continuous datasets called Adaptive Discretization Intervals (ADI), an explicit default rule mechanism and a fitness function based on the Minimum Description Lenghth (MDL) principle to generate accurate and compact solutions. It is intended to deal with problems that can be solved using very compact rule sets.

References in zbMATH (referenced in 7 articles )

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  1. Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O’Reilly: Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning (2015) not zbMATH
  2. Acilar, Ayşe Merve; Arslan, Ahmet: A novel approach for designing adaptive fuzzy classifiers based on the combination of an artificial immune network and a memetic algorithm (2014) ioport
  3. Cano, Alberto; Zafra, Amelia; Ventura, Sebastián: An interpretable classification rule mining algorithm (2013) ioport
  4. Franco, María A.; Krasnogor, Natalio; Bacardit, Jaume: GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning (2013) ioport
  5. Policicchio, Veronica L.; Pietramala, Adriana; Rullo, Pasquale: GAMoN: discovering (M)-of-(N^\neg, \lor) hypotheses for text classification by a lattice-based genetic algorithm (2012)
  6. Yang, Jiadong; Xu, Hua; Jia, Peifa: Effective search for Pittsburgh learning classifier systems via estimation of distribution algorithms (2012) ioport
  7. Bacardit, Jaume; Krasnogor, Natalio: Performance and efficiency of memetic Pittsburgh learning classifier systems (2009) ioport