Lazy learning algorithms, exempli ed by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single \best" decision tree during the training phase, and this tree is then used to classify test instances. The tests a...

Conventional binary classification trees such as CART either split the data using axis-aligned hyperplanes or they perform a computationally expensive search in the continuous space of hyperplanes with unrestricted orientations. We show that the limitations of the former can be overcome without resorting to the latter. For every pair of training data-points, there is one hyperplane that is orth...

In this research, we have compared three different attribute selection measures algorithms. We have used ID3 algorithm, C4.5 algorithm and CART algorithm. All these algorithms are decision tree based algorithm. We have got the accuracy of three different algorithms and we observed that the accuracy of ID3 algorithm is greater than C4.5 algorithm. But the accuracy of CART algorithm is greater th...

A non-greedy approach for constructing globally optimal multivariate decision trees with xed structure is proposed. Previous greedy tree construction algorithms are locally optimal in that they optimize some splitting criterion at each decision node, typically one node at a time. In contrast, global tree optimization explicitly considers all decisions in the tree concurrently. An iterative line...

panic attacks are discrete episodes of intense fear or discomfort accompanied by symptoms such as palpitations, shortness of breath, sweating, trembling, derealization and a fear of losing control or dying. although panic attacks are required for a diagnosis of panic disorder, they also occur in association with a host of other disorders listed in the 5h version of the diagnostic and statistica...

Algorithms for efficiently finding optimal alphabetic decision trees for known probability distributions are well established and commonly used. However, such algorithms generally assume that the cost per decision is uniform and thus independent of the outcome of the decision. The few algorithms without this assumption instead use one cost if the decision outcome is “less than” and another cost...

There exist various tools for knowledge representation, modelling, and simulation in Artificial Intelligence. We have designed and built a software tool (expert system shell) called McESE (McMaster Expert System Environment) that processes a set of production (decision) rules of a very general form. Such a production (decision) set can be equivalently symbolized as a decision tree. In real life...

Key ideas from statistical learning theory and support vector machines are generalized to decision trees. A support vector machine is used for each decision in the tree. The \optimal" decision tree is characterized, and both a primal and dual space formulation for constructing the tree are proposed. The result is a method for generating logically simple decision trees with multivariate linear o...

We study impurity-based decision tree algorithms such as CART, C4.5, etc., so as to better understand their theoretical underpinnings. We consider such algorithms on special forms of functions and distributions. We deal with the uniform distribution and functions that can be described as unate functions, linear threshold functions and readonce DNF. For unate functions we show that that maximal ...

People are interested in developing a machine learning algorithm that works well in all situations. I proposed and studied a machine learning algorithm that combines two widely used algorithms: decision trees and instance-based learning. I combine these algorithms by using the decision trees to determine the relevant attributes and then running the noise-resistant instancebased learning algorit...