Preprocessing by a Cost-sensitive Literal Reduction Algorithm: Reduce 1

This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the eeciency of learning. In addition, cost-sensitive feature elimination can be eeective for reducing costs of induced hypotheses.