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Publicação
Multidimensional genetic programming for multiclass classification
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Orientador(es)
Resumo(s)
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.
Descrição
La Cava, W., Silva, S., Danai, K., Spector, L., Vanneschi, L., & Moore, J. H. (2019). Multidimensional genetic programming for multiclass classification. Swarm and Evolutionary Computation, 44(February), 260-272. DOI: 10.1016/j.swevo.2018.03.015
Palavras-chave
Dimensionality reduction Feature extraction Feature selection Feature synthesis Genetic programming Multiclass classification General Computer Science General Mathematics