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—On-line learning algorithms often have to operate in the presence of concept drift (i.e., the concepts to be learnt can change with time). This paper presents a new categorization for concept drift, separating drifts according to different criteria into mutually exclusive and non-heterogeneous categories. Moreover, although ensembles of learning machines(More)
A brief overview of the history of the development of decision tree induction algorithms is followed by a review of techniques for dealing with missing attribute values in the operation of these methods. The technique of dynamic path generation is described in the context of tree-based classiication methods. The waste of data which can result from casewise(More)
A database on 2692 dyspeptic patients over the age of 40 was established, consisting of 73 epidemiological and clinical variables. A tree-based machine learning algorithm (PREDICTOR) was applied to this database, in order to attempt to find rules which would classify patients into 2 groups, i.e., those suffering from gastric or oesophageal cancer, and the(More)
An alternative approach to uncertain inference in expert systems is described which might be regarded as a synthesis of techniques from automatic induction and mathematical statistics. It utilises a type of pattern matching in which comparisons are made between new cases (as yet unclassified) and a database of past cases (in which the outcome is known). The(More)