Data Mining Process Optimization in Computational Multi-agent Systems

Abstrakt

In this paper, we present an agent-based solution of metalearning problem which focuses on optimization of data mining processes. We exploit the framework of computational multi-agent systems in which various meta-learning problems have been already studied, e.g. parameter-space search or simple method recommendation. In this paper, we examine the effect of data preprocessing for machine learning problems. We perform the set of experiments in the search-space of data mining processes which is constituted by combinations of preprocessing methods with classifiers. The optimization takes place with respect to two criteria - error-rate and model learning time, which are partially complementary. The results of the consistent search algorithm on a number of classification data-sets are shown and the advantage of automated preprocessing augmentation of method recommendation is demonstrated.