Now showing items 1-6 of 6
Tree Ensembles on the Induced Discrete Space
(Institute of Electrical and Electronics Engineers Inc., 2016-05)
Decision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and ...
Searching for the Optimal Ordering of Classes in Rule Induction
Rule induction algorithms such as Ripper, solve a K > 2 class problem by converting it into a sequence of K - 1 two-class problems. As a usual heuristic, the classes are fed into the algorithm in the order of increasing ...
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 ...
Incremental construction of classifier and discriminant ensembles
(ELSEVIER SCIENCE INC, 2009-04-15)
We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier ...
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 ...
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 ...