Supervised Learning Using Local Analysis in an Optimal-Path Forest

Author

Amorim, Willian Paraguassu ; De Carvalho, Marcelo H.

Author_Institution

Inst. of Comput., Fed. Univ. of Mato Grosso do Sul, Campo Grande, Brazil

fYear

2012

fDate

22-25 Aug. 2012

Firstpage

330

Lastpage

335

Abstract

In this paper, we present an OPF-LA (Optimal Path Forest -- Local Analysis), a new learning model proposal. OPF-LA is a heuristic that uses local information for selecting prototypes that, in turn, will be used to classify new data. It employs the main ideas of an OPF classifier, suggesting a new procedure in the data training phase. Experimental results show the advantages in efficiency and accuracy over classical learning algorithms in areas such as Support Vector Machines (SVM), Artificial Neural Networks using Multilayer Perceptrons (MP), and Optimal Path Forest (OPF), in several applications.

Keywords

learning (artificial intelligence); multilayer perceptrons; pattern classification; support vector machines; MP; OPF classifier; OPF-LA; SVM; artificial neural networks; data classification; data training phase; local analysis; local information; multilayer perceptrons; optimal path forest -- local analysis; supervised learning; support vector machines; Accuracy; Feature extraction; Prototypes; Support vector machines; Training; Transform coding; Vegetation; Optimal-Path Forest; Supervised classifiers;

fLanguage

English

Publisher

ieee

Conference_Titel

Graphics, Patterns and Images (SIBGRAPI), 2012 25th SIBGRAPI Conference on

Conference_Location

Ouro Preto

ISSN

1530-1834

Print_ISBN

978-1-4673-2802-9

Type

conf

DOI

10.1109/SIBGRAPI.2012.53

Filename

6382775