Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
Application of image texture analysis to improve land cover classification
WSEAS Transactions on Computers
Using image texture analysis to improve land cover classification
MATH'08 Proceedings of the 13th WSEAS international conference on Applied mathematics
Ensemble classification based on generalized additive models
Computational Statistics & Data Analysis
Multiple classifier systems in remote sensing: from basics to recent developments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Contribution of airborne full-waveform lidar and image data for urban scene classification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Environmental Modelling & Software
International Journal of Intelligent Systems in Accounting and Finance Management
A novel approach to estimate proximity in a random forest: An exploratory study
Expert Systems with Applications: An International Journal
Grass, scrub, trees and random forest
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Spectral-spatial classification of hyperspectral imagery based on Random Forests
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
A survey of multiple classifier systems as hybrid systems
Information Fusion
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Random Forests are considered for classification of multisource remote sensing and geographic data. Various ensemble classification methods have been proposed in recent years. These methods have been proven to improve classification accuracy considerably. The most widely used ensemble methods are boosting and bagging. Boosting is based on sample re-weighting but bagging uses bootstrapping. The Random Forest classifier uses bagging, or bootstrap aggregating, to form an ensemble of classification and regression tree (CART)-like classifiers. In addition, it searches only a random subset of the variables for a split at each CART node, in order to minimize the correlation between the classifiers in the ensemble. This method is not sensitive to noise or overtraining, as the resampling is not based on weighting. Furthermore, it is computationally much lighter than methods based on boosting and somewhat lighter than simple bagging. In the paper, the use of the Random Forest classifier for land cover classification is explored. We compare the accuracy of the Random Forest classifier to other better-known ensemble methods on multisource remote sensing and geographic data.