Publication: Improving optimum-path forest classification using confidence measures
Loading...
Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Type
Work presented at event
Access right
Acesso aberto
Abstract
Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this work, we presented an improved version of the Optimum-Path Forest classifier, which learns a score-based confidence level for each training sample in order to turn the classification process “smarter”, i.e., more reliable. Experimental results over 20 benchmarking datasets have showed the effectiveness and efficiency of the proposed approach for classification problems, which can obtain more accurate results, even on smaller training sets.
Description
Keywords
Confidence measures, Optimum-path forest, Supervised learning
Language
English
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 619-625.
