Department of Computer Science,
Clayton 3168, Australia.
Low prediction accuracy (LPA) problem can be caused by several
factors. Overfitting low quality data and being misled by them
seem to be the significant ones. Traditional inductive learning
algorithms derive rules by working on point values of each
attribute. This approach could easily be misled by low quality
data and the derived rules could overfit them and cause LPA problem.
This paper presents an alternative way which derives rules by working
on the fields of attributes with respect to classes, rather than on
on individual point values of attributes. The experimental results
results show that field learning achieved a higher prediction accuracy
rate and a higher prediction ability rate on new unseen test cases
which is particularly true when the learning is performed on large low
low quality data.
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