Classification trees: an alternative to traditional land cover classifiers

Hansen, Matthew C.; Dubayah, R.; DeFries, Ruth

doi:10.1080/01431169608949069

Cited by 372 publications

(173 citation statements)

References 5 publications

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“…Tomando como base os pensamentos de [Hansen et al,1996], [Frield; Brodley, 1997], optou-se por trabalhar com árvores de decisão por sua classificação ser mais objetiva, prover fácil interpretação, bem como ser computacionalmente eficiente. Além disso, [Chikalov, 2011], discorre que as árvores oferecem meios que direcionam ao conhecimento proposicional, a fim de auxiliar no processo de tomada de decisão e classificação preditiva de objetos, tais como o desempenho do aluno no AVA.…”

Section: Introductionunclassified

Utilização de técnicas de mineração de dados educacionais para predição de desempenho de alunos de EaD em ambientes virtuais de aprendizagem

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Burlamaqui²,

Valentim

et al. 2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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Section: Introductionunclassified

Utilização de técnicas de mineração de dados educacionais para predição de desempenho de alunos de EaD em ambientes virtuais de aprendizagem

Rabelo

Burlamaqui²,

Valentim

et al. 2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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“…The population is initialized randomly, using the VDs of each feature (12) as the random number distribution. Exceptionally, if for some chromosome the best VD is higher that 0.8, we inactivate all other features, leaving only one variable active.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Apart from the accuracy of the resulting thematic map, the remote sensing research community has recently focused on the interpretability of the considered classification model. Towards this direction, rule-based classifiers 11 and decision trees 12,13 have been considered. Such models provide a better understating of the underlying physical relations of the classification problem at hand, which is useful from an operational remote sensing perspective, especially for hyperspectral imagery, where prior knowledge is rather limited.…”

Section: Introductionmentioning

confidence: 99%

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Stavrakoudis¹,

Galidaki²,

Gitas³

et al. 2012

IJCIS

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This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles of the iterative rule learning (IRL) approach, whereby a rule extraction algorithm (REA) is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.

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“…Non-parametric tree-based classifiers are gaining consideration in remote sensing studies for their robust predictive capacity and their ability to integrate large multidimensional datasets that have multicollinearity amongst input covariates (Hansen, Dubayah and DeFries 1996). Two such methods are random forests and conditional inference trees (CTree) (Breiman 2001, Hothorn, Hornik andZeileis 2006).…”

Section: Tree-based Classifiersmentioning

confidence: 99%

Mapping debris-covered glaciers in the Cordillera Blanca, Peru : an object-based image analysis approach.

Biddle¹

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Biddle,, Donald J. 1982-, "Mapping debris-covered glaciers in the Cordillera Blanca, Peru : an object-based image analysis approach." (2015). ACKNOWLEDGEMENTSThere are so many people without whose guidance and support this thesis would not have come to fruition. To thank them all by name would be major work in its own right! If I must pare the list for this limited space it should begin with my advisors, Dr. Keith Mountain and Dr. Andrea Gaughan. They challenged me to be the best version of myself, even -nay -especially when it was not the easiest way forward. Thank you also to Dr.Michael Croasdaile for putting his geographer's hat back on to see me through this process, and providing an invaluable outside perspective. A very special thank you to Dr.Carol Hanchette, for her patient encouragement over the (many) years it has taken to reach this point. She reminded me that yes, I can do this. Thanks to Forrest Stevens for the crash course in R coding and machine learning that led to a major breakthrough that helped push me over the hump with this project. My brain still hurts. To my lovely wife Nicole, thank you for putting up with me through the deepest moments of frustration, and being a constant companion, confidante, and comedy sidekick…not just now, but always. Accurate remote-sensing based inventories of glacial ice are often hindered by the presence of supraglacial debris cover. Attempts at automated mapping of debris-covered glacier areas from remotely-sensed multispectral data have met with limited success due to the spectral similarity of supraglacial debris to nearby bedrock, moraines, and fluvial deposition features. Data-fusion approaches leveraging terrain and/or thermal data with multispectral data have yielded improved results in certain geographic regions, but remain unproven in others. This research builds on the data-fusion approaches from the literature and explores the efficacy of object-based image analysis (OBIA) and tree-based machine learning classifiers using Landsat OLI imagery and SRTM elevation data, in effort to map debris-covered glaciers in the Cordillera Blanca range of Peru. Results suggest that the OBIA and machine learning methods render advantages over traditional methods given the unique morphological settings associated with debris-covered glaciers.

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Classification trees: an alternative to traditional land cover classifiers

Cited by 372 publications

References 5 publications

Utilização de técnicas de mineração de dados educacionais para predição de desempenho de alunos de EaD em ambientes virtuais de aprendizagem

Utilização de técnicas de mineração de dados educacionais para predição de desempenho de alunos de EaD em ambientes virtuais de aprendizagem

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Mapping debris-covered glaciers in the Cordillera Blanca, Peru : an object-based image analysis approach.

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