Multidimensional genetic programming for multiclass classification

William La Cava, Sara Silva, Kourosh Danai, Lee Spector, Leonardo Vanneschi, Jason H. Moore

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)
41 Downloads (Pure)


We describe a new multiclass classification method that learns multidimensional feature transformations using genetic programming. This method optimizes models by first performing a transformation of the feature space into a new space of potentially different dimensionality, and then performing classification using a distance function in the transformed space. We analyze a novel program representation for using genetic programming to represent multidimensional features and compare it to other approaches. Similarly, we analyze the use of a distance metric for classification in comparison to simpler techniques more commonly used when applying genetic programming to multiclass classification. Finally, we compare this method to several state-of-the-art classification techniques across a broad set of problems and show that this technique achieves competitive test accuracies while also producing concise models. We also quantify the scalability of the method on problems of varying dimensionality, sample size, and difficulty. The results suggest the proposed method scales well to large feature spaces.

Original languageEnglish
Pages (from-to)260-272
Number of pages12
JournalSwarm and Evolutionary Computation
Issue numberFebruary
Early online date1 Jan 2018
Publication statusPublished - Feb 2019


  • Dimensionality reduction
  • Feature extraction
  • Feature selection
  • Feature synthesis
  • Genetic programming
  • Multiclass classification


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