A real-coded genetic algorithm for constructive induction

Abstract

Constructive Induction (CI) is a process applied to representation space prior to learning algorithms. This process transforms original representation space into a representation that highlights regularities. In this new improved space learning algorithms work more effectively, generating better solutions. Most CI methods apply a greedy strategy to improve representation space. Greedy methods might converge to local optima, when search space is complex. Genetic Algorithms (GA) as a global search strategy is more effective in such situations. In this paper, a real-coded GA (RGACI) model is represented for CI. This model optimizes the representation space by discretization of feature's values, constructing new features with a GA and evaluation and selection of features upon a PNN Classifier accuracy. Results reveal that PNN Classifier accuracy will improved considerably after it is integrated with RGACI model.

Class-dependent projection based method for text categorization

“…In the other methods including Fisher LDA (Sebastiani, 2002) and PCA (Cappelli et al, 2001), the data are projected into a reduced space which would characterize or separate classes of data. Projections also have been used to constructed relevant attributes from lowlevel attributes or to reformulate the pattern recognition problem by constructing more relevant features (Estébanez et al, 2005), such as the method of feature induction and constructive induction (HajAbedi, 2009). These techniques for space transformation can be called of global, since the projections are performed over the entire dataset taken together.…”