NOEMON: An Intelligent Assistant for Classi er Selection

The selection of an appropriate classiication model and algorithm is crucial for performing eeective classiication on a dataset. This selection task is impeded by two factors: First, there are many performance criteria, and the behaviour of a classiier varies considerably with them. Second, a classiier's performance is strongly aaected by the characteristics of the dataset. Classiier selection implies mastering a lot of background information on the dataset, the models and the algorithms in question. An intelligent assistant can reduce this eeort by inducing helpful suggestions from background information. In this study, we present such an assistant, NOEMON. For each registered classiier, NOEMON measures its performance for a collection of datasets. Rules are induced from those measurements and accommodated in a knowledge base. The suggestion on the most appropriate classiier(s) for a dataset is then based on those rules. Results on the performance of an initial prototype are also given.