Noise-Tolerant Instance-Based Learning Algorithms

Abstract

Several published reports show that instance-based learning algorithms yield high classification accuracies and have low storage requirements during supervised learning applications. However, these learning algorithms are highly sensitive to noisy training instances. This paper describes a simple extension of instance-based learning algorithms for detecting and removing noisy instances from concept descriptions. This extension requires evidence that saved instances be significantly good classifiers before it allows them to be used for subsequent classification tasks. We show that this extension's performance degrades more slowly in the presence of noise, improves classification accuracies, and further reduces storage requirements in several artificial and real-world database applications.