title: Nearest neighbour classification with heterogeneous proximity functions.
abstract: Heterogeneous proximity functions are similarity or distance functions which process data differently according to the scale of attributes. We compared two Minkowskian distance functions with three heterogeneous proximity functions to test whether these functions were better in data sets with attributes of mixed type. Significant differences in nearest neighbour classification accuracy were found in 11 of all the 21 data sets. City-block and Euclidean distance functions outperformed Gower's similarity function and Heterogeneous Euclidean-Overlap Metric, while Heterogeneous Value Difference Metric (HVDM) was better than the other functions. HVDM classified mixed data best, because it treats nominal attributes more carefully than the other functions.
authors.affiliation: Department of Computer Science, 33014 University of Tampere, Finland. jpl@cs.uta.fi
authors.forename: J
authors.initials: J
authors.lastname: Laurikkala
authors.forename: M
authors.initials: M
authors.lastname: Juhola
mesh_terms: D001185:Artificial Intelligence; D003625:Data Collection / Q000706:statistics & numerical data; D020000:Decision Support Systems, Clinical; D005103:Expert Systems; D006801:Humans; D013223:Statistics as Topic