LBR is a highly accurate classification algorithm, which
lazily constructs a single Bayesian rule for each test
instance at classification time. However, its computational
complexity of attribute-value pair selection is quadratic to
the number of attributes. This fact incurs high
computational costs, especially for datasets of high
dimensionality. To solve the problem, this paper proposes
an efficient algorithm LBR-Meta to construct lazy
Bayesian rules in a heuristic way. It starts with the global
classifier trained on the whole instance space. At each step,
the attribute-value pair that best differentiates the
performance of the current local classifier is selected and
used to reduce the current subspace to a further smaller
subspace for the next step. The selection strategy used has
a linear computational complexity with respect to the
number of attributes, in contrast to the quadratic
complexity in LBR. Experimental results manifest that
LBR-Meta has achieved comparable accuracy with LBR,
but at a much lower computational cost.
Cite as: Xie, Z. (2008). LBR-Meta: An Efficient Algorithm for Lazy Bayesian Rules. In Proc. Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. Roddick, J. F., Li, J., Christen, P. and Kennedy, P. J., Eds. ACS. 33-39.
(from crpit.com)
(local if available)