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Combining symbolic classifiers from multiple inducers
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DOI：10.1016/S0950-7051(02)00021-7.png)
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Abstract
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Classification algorithms for large databases have many practical applications in data mining. Whenever a dataset is too large for a particular learning algorithm to be applied, sampling can be used to scale up classifiers to massive datasets. One general approach associated with sampling is the construction of ensembles. Although benefits in accuracy can be obtained from the use of ensembles, one problem is their interpretability. This has motivated our work on trying to use the benefits of combining symbolic classifiers, while still keeping the symbolic component in the learning system. This idea has been implemented in the XRULER system. We describe the XRULER system, as well as experiments performed to evaluate it on 10 datasets. The results show that it is possible to combine symbolic classifiers into a final symbolic classifier with increase in the accuracy and decrease in the number of final rules. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords:
combining classifiers
machine leaming
data mining
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