Now showing items 1-5 of 5
Design and analysis of classifier learning experiments in bioinformatics: survey and case studies
(IEEE COMPUTER SOC, 2012-12)
In many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design ...
Cost-conscious comparison of supervised learning algorithms over multiple data sets
(ELSEVIER SCI LTD, 2012-04)
In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization ...
Omnivariate Rule Induction Using a Novel Pairwise Statistical Test
(IEEE Computer Soc, 2013-09)
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 ...
Model selection in omnivariate decision trees using Structural Risk Minimization
(ELSEVIER SCIENCE INC, 2011-12)
As opposed to trees that use a single type of decision node, an omnivariate decision tree contains nodes of different types. We propose to use Structural Risk Minimization (SRM) to choose between node types in omnivariate ...
VC-Dimension of Univariate Decision Trees
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015-02)
In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension ...