In this paper, a hybrid learning approach named HDT is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary hybrid decision tree according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. I...

This paper extends recent work on decision tree grafting. Grafting is an inductive process that adds nodes to inferred decision trees. This process is demonstrated to frequently improve predictive accuracy. Superficial analysis might suggest that decision tree grafting is the direct reverse of pruning. To the contrary, it is argued that the two processes are complementary. This is because, like...

Often the medical decision maker will be faced with a sequential decision problem involving decisions that lead to different outcomes depending on chance. If the decision process involves many sequential decisions, then the decision problem becomes difficult to visualize and to implement. Decision trees are indispensable graphical tools in such settings. They allow for intuitive understanding o...

We agree with Ms Cameron that careful experimental design is required when doing clinical research on living subjects. In addition to being important to clinical research, it also is required when doing experimental pain research such as ours. We, like Ms Cameron, also look forward to seeing in the Journal a clinical study regarding the different effects of TENS applied to various sets of acupu...

We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can man...

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 ...