… clause will have a very small weight, and the corresponding node in the decision tree
generated using our approach will be fairly deep, ie, it will be considered towards the end. On the …

… that the positioning systems based on weighted decision trees have a lower computational
… multiple weighted decision trees. Decision trees build classification models in the form of a …

… that is, we give each training instance different weights indicating its relations to the … weights
of instances to build decision trees. We employ emerging patterns (EPs) to compute weights …

… entropy and cost measures to set the decision rules at each node. At each … decision tree.
Algorithm 1 shows the structured process improved associated weight improved decision tree …

… from weighted information entropy (Guiasu 1971), this section introduces another oblique
decision tree based on instance weights. The basic idea is to consider different weights of …

… on a distance function in decision trees, called modified bagging (or weighted Bagging) in
this … on various tree levels for all sample sizes. An interesting observation is that the weighted …

… on decision stumps, which are decision trees that consist of a single question. In alternating
decision trees, the levels of the tree … and nodes that contain weights and have an arbitrary …

… of decision tree models. The difficulty lies in calculating the prior probability of the trees. …
Because we weight the trees equally when calculating the average, we require a systematic …

… This paper proposes a novel algorithm called decision tree-based weighted majority voting
(… decision trees are built to calculate the weight of each feature (worker). Finally weighted …

… to investigate a hybrid scheme based on weighted fuzzy decision trees (wFDT), as an efficient
… decision trees, the role of fuzzy logic becomes very crucial in softening the sharp decision …