Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on proprietary of model provider and private input data client. In this paper, we design, implement, evaluate a new system that allows efficient outsourcing inference. Our significantly improves upon prior art in overall online end-to-end secure service latency at as well local-s...

Decision Tree Induction (DTI) is an important step of the segmentation methodology. It can be viewed as a tool for the analysis of large datasets characterized by high dimensionality and nonstandard structure. Segmentation follows a nonparametric approach, since no hypotheses are made on the variable distribution. The resulting model has the structure of a tree graph. It is considered a supervi...

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree—“Extremely Fast Decision Tree”, a minor modification to theMOA implementation of Hoeffding Tree—obtains significantly superior prequential accuracy onmost...

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...