Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

BACKGROUND Various species of forest trees are commonly used for ornamental purposes and are therefore frequently found in non-forest ecosystems. These trees constitute a significant component of the trees outside forests (TOF). Although increasingly recognized as prominent feature of agricultural lands and built-up areas, not much is known, however, about TOF since they are generally absent fr...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...