K*: An Instance-based Learner Using an Entropic Distance Measure [chapter]

John G. Cleary, Leonard E. Trigg
1995 Machine Learning Proceedings 1995  
The use of entropy as a distance measure has several benefits. Amongst other things it provides a consistent approach to handling of symbolic attributes, real valued attributes and missing values. The approach of taking all possible transformation paths is discussed. We describe K*, an instance-based learner which uses such a measure, and results are presented which compare favourably with several machine learning algorithms.
doi:10.1016/b978-1-55860-377-6.50022-0 dblp:conf/icml/ClearyT95 fatcat:qmyfxxbk4fg6rok353x3umou6y