Comparison of various routines for unknown attribute value processing the covering paradigm

Simple inductive learning algorithms assume that all attribute values are available. The well-known Quinlan's paper¹ discusses quite a few routines for the processing of unknown attribute values in the TDIDT family and analyzes seven of them. This paper introduces five routines for the processing of unknown attribute values that have been designed for the CN4 learning algorithm, a large extension of the well-known CN2. Both algorithms CN2 and CN4 induce lists of decision rules from examples applying the covering paradigm. CN2 offers two ways for the processing of unknown attribute values. The CN4's five routines differ in style of matching complexes with examples (objects) that involve unknown attribute values. The definition of matching is discussed in detail in the paper. The strategy of unknown value processing is described both for learning and classification phases in individual routines. The results of experiments with various percentages of unknown attribute values on real-world (mostly medical) data are presented and performances of all five routines are compared.