Smaldon, J. and Freitas, A.A.
(2006)
A New Version of the Ant-Miner Algorithm Discovering Unordered Rule Sets.
In: 2006 Genetic and Evolutionary Computation Conference, 8-12 July 2006 , Seattle, Washington (USA).
Abstract
The Ant-Miner algorithm, first proposed by Parpinelli and
colleagues, applies an ant colony optimization heuristic to the
classification task of data mining to discover an ordered list of
classification rules. In this paper we present a new version of the
Ant-Miner algorithm, which we call Unordered Rule Set Ant-Miner,
that produces an unordered set of classification rules. The proposed
version was evaluated against the original Ant-Miner algorithm in
six public-domain datasets and was found to produce comparable
results in terms of predictive accuracy. However, the proposed
version has the advantage of discovering more modular rules, i.e.,
rules that can be interpreted independently from other rules – unlike
the rules in an ordered list, where the interpretation of a rule requires
knowledge of the previous rules in the list. Hence, the proposed
version facilitates the interpretation of discovered knowledge, an
important point in data mining.
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