Lazy Learning Algorithms for Problems with Many Binary Features and Classes

We have designed several new lazy learning algorithms for learning problems with many binary features and classes. This particular type of learning task can be found in many machine learning applications but is of special importance for machine learning of natural language. Besides pure instance-based learning we also consider prototype-based learning, which has the big advantage of a large reduction of the required memory and processing time for classification. As an application for our learning algorithms we have chosen natural language database interfaces. In our interface architecture the machine learning module replaces an elaborate semantic analysis component. The learning task is to select the correct command class based on semantic features extracted from the user input. We use an existing German natural language interface to a production planning and control system as a case study for our evaluation and compare the results achieved by the different lazy learning algorithms.