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| A Heuristic Algorithm to Attribute Reduction with Simple Common-cost |
| Zhonghui Liu, Yu Fang, Ji Dong, Fan Min |
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Abstract Cost-sensitive learning is an important issue in both data mining
and machine learning. Most existing research works focus on decision
systems where test-costs are additive. However, in some
applications there is a common-cost within a group of tests. In this
paper, we design an attribute reduction algorithm with a heuristic
function and a parameter adjusting scheme to deal with this
situation. The heuristic function has two parameters serving as the
reward and the penalty exponent, respectively. The parameter
adjusting scheme is based on the competition approach. Experimental
results on four UCI (University of California-Irvine) datasets
indicate that the algorithm obtains optimal reducts in most cases.
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| DOI:10.12733/jics20105758 |
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