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Multivariate Statistical Tests for Comparing Classification Algorithms

Summary: Multivariate Statistical Tests for Comparing
Classification Algorithms
Olcay Taner Yildiz1
, ¨Ozlem Aslan2
, and Ethem Alpaydin2
Dept. of Computer Engineering, I¸sik University, TR-34980, Istanbul, Turkey
Dept. of Computer Engineering, Bogazi¸ci University, TR-34342, Istanbul, Turkey
Abstract. The misclassification error which is usually used in tests to
compare classification algorithms, does not make a distinction between
the sources of error, namely, false positives and false negatives. Instead
of summing these in a single number, we propose to collect multivariate
statistics and use multivariate tests on them. Information retrieval uses
the measures of precision and recall, and signal detection uses true pos-
itive rate (tpr) and false positive rate (fpr) and a multivariate test can
also use such two values instead of combining them in a single value, such
as error or average precision. For example, we can have bivariate tests for
(precision, recall) or (tpr, fpr). We propose to use the pairwise test based
on Hotelling's multivariate T2


Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University


Collections: Computer Technologies and Information Sciences