ReliefF is a feature mining technique, which has been successfully used in data mining applications. However, ReliefF is sensitive to the definition of relevance that is used in its implementation and when handling a large data set, it is computationally expensive. This paper presents an optimisation (Feature Selection via Supervised Model Construction) for data transformation and starter selection, and evaluates its effectiveness with C4.5. Experiments indicate that the proposed method gave improvement of computation efficiency whilst maintaining classification accuracy of trial data sets.