A parallel hill-climbing algorithm to generate a subset of irreducible testors

The generation of irreducible testors from a training matrix is an expensive computational process: all the algorithms reported have exponential complexity. However, for some problems there is no need to generate the entire set of irreducible testors, but only a subset of them. Several approaches have been developed for this purpose, ranging from Univariate Marginal Distribution to Genetic Algorithms. This paper introduces a parallel version of a Hill-Climbing Algorithm useful to find a subset of irreducible testors from a training matrix. This algorithm was selected because it has been one of the fastest algorithms reported in the state-of-the-art on irreducible testors. In order to efficiently store every different irreducible testor found, the algorithm incorporates a digital-search tree. Several experiments with synthetic and real data are presented in this work. © 2014, Springer Science%2bBusiness Media New York.

Binary trees; Feature selection; Hill-climbing; Irreducible testors; Pattern recognition Binary trees; Feature extraction; Genetic algorithms; Pattern recognition; Trees (mathematics); Computational process; Exponential complexity; Hill climbing; Hill climbing algorithms; Irreducible testors; Marginal distribution; Parallel version; Synthetic and real data; Algorithms

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