Meta-Learner for Unknown Attribute Values Processing: Dealing with Inconsistency of Meta-Databases

摘要

Efficient robust data mining algorithms should comprise some routines for processing unknown (missing) attribute values when acquiring knowledge from real-world databases because these data usually contain a certain percentage of missing values. The paper Bruha and Franek (1996) figures out that each dataset has more or less its own ‘favourite’ routine for processing unknown attribute values. It evidently depends on the magnitude of noise and source of unknownness in each dataset. One possibility how to choose an efficient routine for processing unknown attribute values for a given database is exhibited in this paper. The covering machine learning algorithm CN4, a large extension of the well-known CN2 algorithm, is used here as an inductive vehicle.