Decision trees have been widely used in machine learning. However, due to some reasons, data collecting in real world contains a fuzzy and uncertain form. The decision tree should be able to handle such fuzzy data. This paper presents a method to construct fuzzy decision tree. It proposes a fuzzy decision tree induction method in iris flower data set, obtaining the entropy from the distance bet...

Searching for binary partition of attribute domains is an important task in Data Mining, particularly in decision tree methods. The most important advantage of decision tree methods are based on compactness and clearness of presented knowledge and high accuracy of classification. In case of large data tables, the existing decision tree induction methods often show to be inefficient in both comp...

Two separate results related to decision-tree complexity are presented. The first uses a topological approach to generalize some theorems about the evasiveness of monotone boolean functions to other classes of functions. The second bounds the gap between the deterministic decision-tree complexity of functions on the permutation group Sn and their zero-error randomized decision-tree complexity.

The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared ...

Abstract Decision trees are a widely used method for classification, both alone and as the building blocks of multiple different ensemble learning methods. The Max Cut decision tree introduced here involves novel modifications to standard, baseline variant classification tree, CART Gini. One modification an alternative splitting metric, Cut, based on maximizing distance between all pairs observ...

Decision tree classification procedures have been largely overlooked in remote sensing applications. In this paper we compare the classification performance of three types of decision trees across three different data sets. The classifiers that are considered include a univariate decision tree, multivariate decision tree, and a hybrid decision tree. Results from an n-fold cross-validation proce...

results overall, the prevalence of unwanted pregnancies was 32.3%. the performance of the models based on the area under the roc curve as the indicator was as follows: artificial neural networks (0.741), decision tree (0.731), and logistic regression (0.712). the highest sensitivity level belonged to the decision tree (73.5%), and the highest specificity level belonged to the artificial neural ...