Journal ArticleDOI
Urban landcover classification from multispectral image data using optimized AdaBoosted random forests
TLDR
The application of AdaBoosted random forest (ABRF), an ensemble of decision trees, to classify landcover segments from multispectral satellite or aerial imagery resulted in the increase in the overall accuracy from 84.42% to 88.8% with an increase in kappa coefficient.Abstract:
With an ever growing need to classify multispectral images, the accuracy of the classification becomes a matter of concern, especially when mapping heterogeneous environments such as urban areas. N...read more
Citations
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Journal ArticleDOI
Classification of stroke disease using machine learning algorithms
Priya Govindarajan,Ravichandran Kattur Soundarapandian,Amir H. Gandomi,Rizwan Patan,Premaladha Jayaraman,Ramachandran Manikandan +5 more
TL;DR: A prototype to classify stroke that combines text mining tools and machine learning algorithms, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes.
Journal ArticleDOI
Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique
TL;DR: The results of the experiment showed that, based on the accuracy improvement of each class, the overall accuracy was improved effectively, which combined advantages from each base classifier, and could be used for analyzing urbanization processes and its impacts.
Journal ArticleDOI
Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods
TL;DR: The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE, Landsat data and ensemble-learning methods to map land cover change over a decade in the Kaski district of Nepal.
Journal ArticleDOI
Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia
TL;DR: In this article, a hybrid reference dataset derived from European Land Use and Coverage Area Frame Survey (LUCAS), CORINE, and Land Parcel Identification Systems (LPIS) LC database was used for vegetation classification.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Journal ArticleDOI
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
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