Decision tree classification technique is one of the most popular techniques in the emerging field of data mining. There are various methods for constructing decision tree. Induced Decision tree (ID3) is the basic algorithm for constructing decision trees. After ID3 various algorithms were proposed by different researchers and authors those are extensions of ID3 algorithm. This paper contains a...

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...