Abstract
The paper provides the results of a performance comparison study of two symbolic learning programs, both based on the AQ15c learning algorithm. The first program uses a single representation space, while the second one utilizes constructive induction, which changes the representation space. The performance of the compared systems was analyzed using three empirical error rates, including the overall, commission and omission error rates. These were determined by applying the hold-out, 10-fold, and leave-one-out sampling methods. Both systems' performance was calculated for individual stages in a multi-stage knowledge-acquisition process. Learning curves and their envelopes were prepared. The study was conducted using a set of 384 optimal designs of wind bracing in steel skeleton structures of tall buildings. The research methodology and the two learning systems used in the experiments are described, all numerical results are provided, and the conclusions of the research are given.
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