From Machine Learning Proceedings of the Twelfth International Conference ML Fast Eective Rule Induction

wcohenresearchattcom Many existing rule learning systems are computationally expensive on large noisy datasets In this paper we evaluate the recently proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems We show that while IREP is extremely e cient it frequently gives error rates higher than those of C and Crules We then propose a num ber of modications resulting in an algo rithm RIPPERk that is very competitive with Crules with respect to error rates but much more e cient on large samples RIPPERk obtains error rates lower than or equivalent to Crules on of bench mark problems scales nearly linearly with the number of training examples and can e ciently process noisy datasets containing hundreds of thousands of example