| One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classifier Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm, which harnesses the power of evolutionary search to mine classification rules from data. These rules are based on piece-wise linear estimates of the Bayes decision boundary within a winnowed subset of the data. Once a sufficient set of these hyper-planes are generated, a genetic algorithm (GA) based 0/1 search is performed to locate combinations of them that enclose class homogeneous regions of the data. It is shown that this approach enables GRaCCE to produce rule sets significantly more compact than those of other DRI methods while achieving a comparable level of accuracy. Since the principle of Occam's razor tells us to always prefer the simplest model that fits the data, the rules found by GRaCCE are of greater utility than those identified by existing methods. |
