Automatic annotation tools are becoming popular since the biologists and curators of databases cannot cope with the volume of sequences to be annotated manually. One way to automate the annotation is to use techniques of symbolic machine learning to derive rules to guide this annotation. However, the training instances tend to have too many attributes, turning the machine learning process difficult and time consuming.