Robust Pruning of Training Patterns for Optimum-Path Forest Classification Applied to Satellite-Based Rainfall Occurrence Estimation

Autor(es)

Papa, Joao Paulo ^[1] ; Falcao, Alexandre Xavier ^[1] ; de Freitas, Greice Martins ^[2] ; Heuminski de Avila, Ana Maria ^[3]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil

^[2] Univ Estadual Campinas, Sch Elect & Comp Engn, BR-13083970 Campinas, SP - Brazil

^[3] Univ Estadual Campinas, Ctr Meteorol & Climat Res Appl Agr, BR-13083970 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE Geoscience and Remote Sensing Letters; v. 7, n. 2, p. 396-400, APR 2010.
• Citações Web of Science: 9

Resumo

The decision correctness in expert systems strongly depends on the accuracy of a pattern classifier, whose learning is performed from labeled training samples. Some systems, however, have to manage, store, and process a large amount of data, making also the computational efficiency of the classifier an important requirement. Examples are expert systems based on image analysis for medical diagnosis and weather forecasting. The learning time of any pattern classifier increases with the training set size, and this might be necessary to improve accuracy. However, the problem is more critical for some popular methods, such as artificial neural networks and support vector machines (SVM), than for a recently proposed approach, the optimum-path forest (OPF) classifier. In this letter, we go beyond by presenting a robust approach to reduce the training set size and still preserve good accuracy in OPF classification. We validate the method using some data sets and for rainfall occurrence estimation based on satellite image analysis. The experiments use SVM and OPF without pruning of training patterns as baselines. (AU)

• Processo FAPESP: 07/52015-0 - Métodos de aproximação para computação visual
• Beneficiário: Jorge Stolfi
• Linha de fomento: Auxílio à Pesquisa - Temático