Multi-objective metaheuristics have previously been applied to partial classification, where the objective is to produce simple, easy to understand rules that describe subsets of a class of interest. While this provides a useful aid in descriptive data mining, it is difficult to see how the rules produced can be combined usefully to make a predictive classifier. This paper describes how, by using a more complex representation of the rules, it is possible to produce effective classifiers for two class problems. Furthermore, through the use of multi-objective genetic programming, the user can be provided with a selection of classifiers providing different trade-offs between the misclassification costs and the overall model complexity.
|Number of pages||15|
|Publication status||Published - 2007|
|Event||Evolutionary Multi-Criterion Optimization 4th International Conference (EMO 2007) - Matsushima, Japan|
Duration: 1 Jan 2007 → …
|Conference||Evolutionary Multi-Criterion Optimization 4th International Conference (EMO 2007)|
|Period||1/01/07 → …|