Learning to Classify Incomplete Examples
Dale Schuurmans, Russell Greiner · The MIT Press eBooks · 1997
Most research on supervised learning assumes the attributes of training and test examples are completely specified. Real-world data, however, is often incomplete. This paper studies the task of learning to classify incomplete test examples, given incomplete (resp., complete) training data. We first show that the performance task of classifying incomplete examples requires the use of default classification functions which demonstrate nonmonotonic classification behavior. We then extend the standard pac-learning model to allow attribute values to be hidden from the classifier, investigate the robustness of various learning strategies, and study the sample complexity of learning classes of default classification functions from examples. 1 Introduction The central task of most expert systems is classifying objects from some domain of application; i.e., determining whether a particular object belongs to a specified class, given a description of that object (Clancey, 1985). For example, a ...