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For classification, ensemble method and neural networks are considered.
Data analysis was done using a robust non-parametric classification ensemble; Discriminant Analysis (DA).
However, despite the existence of many algorithms for online classification, ensemble learning algorithms for online regression do not exist.
We present a new weighted voting classification ensemble method, called WAVE, that uses two weight vectors: a weight vector of classifiers and a weight vector of instances.
Based on the features of data in practical systems, we propose a one-class classification ensemble method incorporating the notion of Feature Subspace with Bagging.
Another set of GAs were used to search for the combination of classifiers that would result in the best classification ensemble accuracy.
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Annual spatially-explicit maps were developed from 2005 to 2011, using monthly composites of MODIS, multiple classifications (ensemble classifier) with decision trees and attributes from auxiliary datasets to characterize 15 land-cover classes in Mexico [53].
Annual spatially-explicit maps were developed from 2005 to 2011, using monthly composites of MODIS, multiple classifications (ensemble classifier) with decision trees and attributes from auxiliary datasets to characterize 15 land-cover classes in Mexico [ 53].
In classification, the ensemble average (of the class probability estimate) can directly be used to characterize the reliability of the individual prediction.
Yet another approach to combine linear classifiers for nonlinear classification is ensemble methods including bagging [3], boosting [8], and random forests [4].
But in a case when a single classifier also can perform very well in classification the ensemble learning algorithm is not as good as incremental learning algorithms.
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