Decision Trees and Their Families in Imbalanced Pattern Recognition: Recognition with and without Rejection - Computer Information Systems and Industrial Management Access content directly
Conference Papers Year : 2014

Decision Trees and Their Families in Imbalanced Pattern Recognition: Recognition with and without Rejection

Abstract

Decision trees are considered to be among the best classifiers. In this work we use decision trees and its families to the problem of imbalanced data recognition. Considered are aspects of recognition without rejection and with rejection: it is assumed that all recognized elements belong to desired classes in the first case and that some of them are outside of such classes and are not known at classifier’s training stage. The facets of imbalanced data and recognition with rejection affect different real world problems. In this paper we discuss results of experiment of imbalanced data recognition on the case study of music notation symbols. Decision trees and three methods of joining decision trees (simple voting, bagging and random forest) are studied. These methods are used for recognition without and with rejection.
Fichier principal
Vignette du fichier
978-3-662-45237-0_22_Chapter.pdf (847.16 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01405585 , version 1 (30-11-2016)

Licence

Attribution

Identifiers

Cite

Wladyslaw Homenda, Wojciech Lesinski. Decision Trees and Their Families in Imbalanced Pattern Recognition: Recognition with and without Rejection. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. pp.219-230, ⟨10.1007/978-3-662-45237-0_22⟩. ⟨hal-01405585⟩
157 View
682 Download

Altmetric

Share

Gmail Facebook X LinkedIn More