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One of the problems of pattern recognition (PR) are datasets with missing attribute values. Therefore, PR algorithms should comprise some routines for processing these missing values. There exist several such routines for each PR paradigm. Quite a few experiments have revealed that each dataset has more or less its own 'favourite' routine for processing missing attribute values. In this paper, we use the machine learning algorithm CN4, a large extension of well-known CN2, which contains six routines for missing attribute values processing. Our system runs these routines independently (at the base level), and afterwards, a metacombiner (at the second level) is used to generate a meta-classifier that makes up the overall decision about the class of input objects. This knowledge combination algorithm splits a training set to S subsets for the training purposes. The parameter S (called ‘foldness’) is the crucial one in the process of meta-learning. The paper focuses on its optimal value. Therefore, the routines used here for the missing attribute values processing are only the vehicles (for the function of the base classifiers); in fact, any PR algorithm for base classifiers could be used. In other words, the paper does not compare various missing attribute processing techniques, but its target is the parameter S.

Classification of Datasets with Missing Values: Two Level Approach