Substructural surrogates for learning decomposable classification problems

Albert Orriols-Puig, Kumara Sastry, David E. Goldberg, Ester Bernadó-Mansilla

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    4 Citas (Scopus)


    This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model, which represents salient interactions between attributes for a given data, (2) a surrogate model, which provides a functional approximation of the output as a function of attributes, and (3) a classification model, which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate. Its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, groups them in linkages groups, and builds maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and also shed light on several improvements to enhance the capabilities of the proposed method.

    Idioma originalInglés
    Título de la publicación alojadaLearning Classifier Systems - 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Papers
    EditorialSpringer Verlag
    Número de páginas20
    ISBN (versión impresa)3540881379, 9783540881377
    EstadoPublicada - 2008
    Evento11th International Workshops on Learning Classifier Systems, WLCS 2007 - London, Reino Unido
    Duración: 8 jul 20078 jul 2008

    Serie de la publicación

    NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volumen4998 LNAI
    ISSN (versión impresa)0302-9743
    ISSN (versión digital)1611-3349


    Conferencia11th International Workshops on Learning Classifier Systems, WLCS 2007
    País/TerritorioReino Unido


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