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

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Abstract

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.

Each of the six routines for unknown attribute value processing (which are available in CN4) is used independently in order to process a given database. Afterwards, a meta-learner is used to derive a meta-classifier that makes up the overall (final) decision about the class of input unseen objects. The entire system is called a meta-combiner.

The meta-database that is formed for the meta-learner could be inconsistent which could decrease the performance of the entire meta-classifier. Therefore, the existing meta-system (Meta-CN4) has been enhanced by a ‘purification’ procedure that appropriately solves up the conflict of inconsistent meta-data.

The paper first surveys the CN4 algorithms including its six routines for unknown attribute value processing. Afterwards, it introduces the methodology of the meta-learner including its enhancement that solves inconsistent meta-databases. Finally, the results of experiments with various percentages of unknown attribute values on real-world data are presented and performances of the meta-classifier and the six base classifiers are then compared. The paper also explains the difference between the meta-combiner (meta-learner) described here and the cross-validation procedure used for obtaining the classification accuracy.

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Authors and Affiliations

  1. Department of Computing & Software, McMaster University, Hamilton, ON, Canada, L8S4K1

    Ivan Bruha

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  1. Ivan Bruha
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Bruha, I. Meta-Learner for Unknown Attribute Values Processing: Dealing with Inconsistency of Meta-Databases. Journal of Intelligent Information Systems 22, 71–87 (2004). https://doi.org/10.1023/A:1025880714026

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