TRADE-OFF BETWEEN REQUIREMENT OF LEARNING AND COMPUTATIONAL COST

Machine learning in real-world situations sometimes starts from an ini tial collection of training instances; learning then proceeds off and on as new training instances come intermittently. The idea of two-phas e learning has then been proposed here for effectively solving the lea rning problems in which training instances came in this two-stage way. Four two-phase learning algorithms based on the learning method PRISM have also been proposed for inducing rules from training instances. T hese alternatives form a spectrum, showing achievement of the requirem ent of PRISM (keeping down the number of irrelevant attributes) heavil y dependent on the spent computational cost. The suitable alternative, as a trade-off between computational costs and achievement to the req uirements, can then be chosen according to the request of the applicat ion domains.