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Communication Dans Un Congrès Année : 2013

The benefits of using multi-objectivization for mining Pittsburgh partial classification rules in imbalanced and discrete data

Résumé

A large number of rule interestingness measures have been used as objectives in multi-objective classification rule mining algorithms. Aggregation or Pareto dominance are commonly used to deal with these multiple objectives. This paper compares these approaches on a partial classification problem over discrete and imbalanced data. After performing a Principal Component Analysis (PCA) to select candidate objectives and find conflictive ones, the two approaches are evaluated. The Pareto dominance-based approach is implemented as a dominance-based local search (DMLS) algorithm using confidence and sensitivity as objectives, while the other is implemented as a single-objective hill climbing using F-Measure as an objective, which combines confidence and sensitivity. Results shows that the dominance-based approach obtains statistically better results than the single-objective approach.
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Dates et versions

hal-00806777 , version 1 (02-04-2013)

Identifiants

  • HAL Id : hal-00806777 , version 1

Citer

Julie Jacques, Julien Taillard, David Delerue, Laetitia Jourdan, Clarisse Dhaenens. The benefits of using multi-objectivization for mining Pittsburgh partial classification rules in imbalanced and discrete data. Genetic and Evolutionary Computation Conference (GECCO 2013), Jul 2013, Amsterdam, Netherlands. pp.543-550. ⟨hal-00806777⟩
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