in the training data sets are properly handled (or controlled or modeled or corrected) appropriately. The present study proposes a new algorithm for decision tree classifier construction. This new algorithm is named as Accurate Decision Tree (ADT) classifier construction. ADT classifiers are more accurate than

at improving the intelligibility of a single classifier. Our goal is the induction of a single, small, yet accurate decision tree. We describe a simple prepruning method (PreC4) that uses cross-validation to determine an appropriate stopping point for tree construction in a reliable manner. In addition

In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-time learning algorithms. One of the most successful algorithms for mining data streams is VFDT. In this paper we extend

of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than

The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one

;quot; pruning method in Mingers (1989). This method produced the most accurate decision trees, and in four of the five domains studied these trees had only 2 or 3 leaves

In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In this tagging method, transition probabilities are estimated using a decision tree. Based on this method

. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment

Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset and derivative pricing theories as well as trading and hedging strategies. In response to this

bounding box trees. At runtime, the algorithm traverses the tree and tests for overlaps between oriented bounding boxes based on a new separating axis theorem, which takes less than 200 operations in practice. It has been implemented and we compare its performance with other hierarchical data structures