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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/18800
Title: Meta-classifier approach with ANN, SVM, rotation forest, and random forest for snow cover mapping
Authors: Nijhawan R.
Raman, Balasubramanian
Das, Josodhir D.
Chaudhuri B.B.
Raman B.
Kankanhalli M.S.
Published in: Proceedings of Advances in Intelligent Systems and Computing
Abstract: This study proposes a meta-classifier approach to combine several individual classifiers for improving the accuracy of snow cover prediction. The results of the proposed approach were compared with the state-of-the-art classifiers: artificial neural network, support vector machine, rotation forest, and random forest. Past studies indicate that such approach has been rarely used for snow cover mapping. This study was conducted in the surrounding region of Gomukh, Uttarakhand, India. The base classifiers were trained on the Landsat ETM+ multispectral images. The performance of the proposed approach was evaluated based on several statistic parameters and receiver operating characteristic (ROC) curve. This study indicates that the proposed model outperformed the recent used state-of-the-art learning algorithms. © Springer Nature Singapore Pte Ltd. 2018.
Citation: Proceedings of Advances in Intelligent Systems and Computing, (2018), 279- 287
URI: https://doi.org/10.1007/978-981-10-7898-9_23
http://repository.iitr.ac.in/handle/123456789/18800
Issue Date: 2018
Publisher: Springer Verlag
Keywords: ANN
Meta-classifier
Random forest
Rotation forest
SVM
Computer vision
Decision trees
Learning algorithms
Mapping
Neural networks
Support vector machines
Individual classifiers
Meta-classifiers
Multispectral images
Random forests
Receiver Operating Characteristic (ROC) curves
Rotation fo
ISBN: 9.79E+12
ISSN: 21945357
Author Scopus IDs: 57192176367
23135470700
7202105464
Author Affiliations: Nijhawan, R., Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Raman, B., Department of Computer Science Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Das, J., Department of Earthquake
Corresponding Author: Nijhawan, R.; Department of Earthquake Engineering, Indian Institute of Technology RoorkeeIndia; email: rahul.deq2014@iitr.ac.in
Appears in Collections:Conference Publications [CS]
Conference Publications [EQ]

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