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Full model selection in the space of data mining operators
Abstract
We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for GAPSO-FMS), in which a GA is used for searching the optimal structure of a data mining solution, and PSO is used for searching the optimal parameter set for a particular structure instance. Given a classification or regression problem, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are applicable to the problem, including data cleansing, data sampling, feature transformation/selection and algorithm operators. The solution can also be represented graphically in a human readable form. Experimental results demonstrate the benefit of the algorithm.
Type
Conference Contribution
Type of thesis
Series
Citation
Sun, Q., Pfahringer, B. & Mayo, M. (2012). Full model selection in the space of data mining operators. GECCO’12 Companion, July 7–11, 2012, Philadelphia, PA, USA.
Date
2012
Publisher
ACM
Degree
Supervisors
Rights
Copyright is held by the authors.