Abstract Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, boosted miss a probabilistic version that encodes prior assumptions about tree structures shares statistical strength between node parameters. Existing work on Bayesian DT depends Markov Chain Monte Carlo (MCMC), wh...

where k is fixed and y is a variable of particular interest. For example x may be customer information for a credit card customer, and y a measure of how credit worthy the customer is. Given a new instance of x we wish to be able to predict the corresponding y. This is essentially a regression problem, though is usually called classification when y is a discrete variable. We call x the independ...

Decision trees find use in a wide range of application domains. They are used in many different disciplines including diagnosis, cognitive science, artificial intelligence, game theory, engineering and data mining. Decision Trees model has two goals: producing an accurate classifier and understanding the predictive structure of the problem. The classification accuracy of decision trees has been...

The tree provides a framework for the calculation of the expected value of each available alternative. The alternative with the maximum expected value is the best choice path based on the information and mind-set of the decisionmakers at the time the decision is made. This best choice path indicates the best overall alternative, including the best subsidiary decisions at future decision steps, ...

Power systems transport an increasing amount of electricity, and in the future, involve more distributed renewables dynamic interactions equipment. The system response to disturbances must be secure predictable avoid power blackouts. can simulated time domain. However, this security assessment (DSA) is not computationally tractable real-time. Particularly promising train decision trees (DTs) fr...

The study of partial match queries on random hierarchical multidimensional data structures dates back to Ph. Flajolet and C. Puech’s 1986 seminal paper on partial match retrieval. It was not until recently that fixed (as opposed to random) partial match queries were studied for random relaxed K-d trees, random standard K-d trees, and random 2-dimensional quad trees. Based on those results it se...

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, drivin...

We give a ln(n) + 1-approximation for the decision tree (DT) problem. We also show that DT does not have a PTAS unless P=NP. An instance of DT is a set of m binary tests T = (T1, . . . , Tm) and a set of n items X = (X1, . . . , Xn). The goal is to output a binary tree where each internal node is a test, each leaf is an item and the average number of tests used to uniquely identify each item (o...

In the presentation, we consider extensions of dynamic programming approach to the study of decision trees as algorithms for problem solving, as a way for knowledge extraction and representation, and as classifiers which, for a new object given by values of conditional attributes, define a value of the decision attribute. These extensions allow us (i) to describe the set of optimal decision tre...

If the company builds a big plant, it must live with it whatever the size of market demand. If it builds a small plant, management has the option of expanding the plant in two years in the event that demand is high during the introductory period; while in the event that demand is low during the introductory period, the company will maintain operations in the small plant and make a tidy profit o...