Substructrual surrogates for learning decomposable classification problems: Implementation and first results
26 March 2007Orriols-Puig A., Sastry K., Goldberg D. E., Bernadó-Mansilla E. (2007). IlliGAL Report No. 2007010. University of Illinois at Urbana-Champaign, Urbana IL. [Full paper - PDF] [Full paper - PS].
Abstract:
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 that 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 and 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 key variable interactions in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.
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