Enhancing classification performance using attribute-oriented functionally expanded data

There are many data pre-processing techniques that aim at enhancing the quality of classifiers induced by machine learning algorithms. Functional expansions (FE) are one of such techniques, which has been originally proposed to aid neural network based classification. Despite of being successfully employed, works reported in the literature use the same functional expansion, with the same expansion size (ES), applied to each attribute that describes the training data. In this paper it is argued that FE and ES can be attribute-oriented and, by choosing the most suitable FE-SE pair for each attribute, the input data representation improves and, as a consequence, learning algorithms can induce better classifiers. This paper proposes, as a pre-processing step to learning algorithms, a method that uses a genetic algorithm for searching for a suitable FE-SE pair for each data attribute, aiming at producing functionally extended training data. Experimental results using functionally expanded training sets, considering four classification algorithms, KNN, CART, SVM and RBNN, have confirmed the hypothesis; the proposed method for searching for FE-SE pairs through an attribute-oriented fashion has yielded statistically significant better results than learning from the original data or by considering the result from the best FE-SE pair for all attributes.