Noise-Tolerant Windowing

Abstract

Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that it may often lead to a decrease in performance, in particular in noisy domains. Following up on previous work, where we have shown that the ability of separate-and-conquer rule learning algorithms to learn rules independently can be exploited for more efficient windowing procedures, we demonstrate in this paper how this property can be exploited to achieve noise-tolerance in windowing. 1 Introduction Windowing is a general technique that aims at improving the efficiency of inductive classification learners. The gain in efficiency is obtained by identifying an appropriate subset of the given training examples, from which a theory of sufficient quality can be induced. Such procedures are also known as subsampling. Windowing has been proposed in (Quinlan, 1983) as a supplement to the inductive decision tree learner ID3 to enable it to tack...