Omnivariate rule induction using a novel pairwise statistical test
Yükleniyor...
Tarih
2013-09
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE Computer Soc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Rule learning algorithms, for example, RIPPER, induces univariate rules, that is, a propositional condition in a rule uses only one feature. In this paper, we propose an omnivariate induction of rules where under each condition, both a univariate and a multivariate condition are trained, and the best is chosen according to a novel statistical test. This paper has three main contributions: First, we propose a novel statistical test, the combined 5 x 2 cv t test, to compare two classifiers, which is a variant of the 5 x 2 cv t test and give the connections to other tests as 5 x 2 cv F test and k-fold paired t test. Second, we propose a multivariate version of RIPPER, where support vector machine with linear kernel is used to find multivariate linear conditions. Third, we propose an omnivariate version of RIPPER, where the model selection is done via the combined 5 x 2 cv t test. Our results indicate that 1) the combined 5 x 2 cv t test has higher power (lower type II error), lower type I error, and higher replicability compared to the 5 x 2 cv t test, 2) omnivariate rules are better in that they choose whichever condition is more accurate, selecting the right model automatically and separately for each condition in a rule.
Açıklama
Anahtar Kelimeler
Rule induction, Model selection, Statistical tests, Support vector machines, Ant colony optimization, Classification trees, Learning algorithms, Decision trees
Kaynak
IEEE Transactions on Knowledge and Data Engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
25
Sayı
9
Künye
Yıldız, O. T. (2013). Omnivariate rule induction using a novel pairwise statistical test. IEEE Transactions on Knowledge and Data Engineering, 25(9), 2105-2118. doi:10.1109/TKDE.2012.155
