The Development of Holte's 1R Classifier

The 1R procedure for machine learning is a very simple one that proves surprisingly effective on the standard datasets commonly used for evaluation. This paper describes the method and discusses two areas that can be improved: the way that intervals are formed when discretizing continuously-valued attributes, and the way that missing values are treated. Then we show how the algorithm can be extended to avoid a problem endemic to most practical machine learning algorithms—their frequent dismissal of an attribute as irrelevant when in fact it is highly relevant when combined with other attributes.

• Type: Conference
• Year: 1995
• Where: ANNES
• Authors: Craig G. Nevill-Manning, Geoffrey Holmes, Ian H. Witten
• Keywords: ANNES 1995, Continuously-valued Attributes, Machine Learning, Machine Learning Algorithms—their, Neural Networks