Feature selection for morphological feature extraction using random forests

Sveinn R. Joelsson*, Jon Atli Benediktsson, Johannes R. Sveinsson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Morphological feature extraction (MFE) has been successfully used to increase classification accuracy and reduce the noise level for classification of aerial images. In this paper we explore feature selection and extraction for MFE using random forests (RFs) for classification and feature selection. The approach is compared to MFE from principal components extracted from the data, by principal component analysis (PCA), which has been successful in the past. The experimental results presented in this paper show that by estimating the most important features of our data set using RFs, and selecting a few of said features for MFE yields equal or better accuracies than by using PCs.

Original languageEnglish
Title of host publicationProceedings of the 7th Nordic Signal Processing Symposium, NORSIG 2006
PublisherIEEE Computer Society
Pages10-13
Number of pages4
ISBN (Print)1424404126, 9781424404124
DOIs
Publication statusPublished - 2006
Event7th Nordic Signal Processing Symposium, NORSIG 2006 - Reykjavik, Iceland
Duration: 7 Jun 20069 Jun 2006

Publication series

NameProceedings of the 7th Nordic Signal Processing Symposium, NORSIG 2006

Conference

Conference7th Nordic Signal Processing Symposium, NORSIG 2006
Country/TerritoryIceland
CityReykjavik
Period7/06/069/06/06

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