Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art. Keywords: classification, tree-structured classifiers, data compaction 1. Introduction Advances in data collection methods, storage and processing technology are providing a unique challenge and opportunity for automated data exploration techniques. Enormous amounts of data are being collected daily from major scientific projects e.g., Human Genome...

. This paper focuses on a unified sight in the field of non-linear feature space partitioning. We present two well-known approaches of growing decision trees from data and show that these methods have a lot in common regarding non-linearity. The aim of this paper is to clarify that the application of simple mathematical operations broadens the capabilities to split the feature space in a non-linear fashion. Keywords: Non-linear and Fuzzy Decision Trees, Feature Space Partitioning 1 Introduction In the research field of supervised learning from examples the accuracy of feature space partitioning is in some cases much more important than the simplicity of the splitting. One important method for more accurate separation of examples belonging to different classes is non-linear feature space partitioning. Especially in the field of growing decision trees from data non-linear partitioning broadens the capabilities of finding good splits in the feature space. One way to achieve the goal of...

Abstract. In the task of classification, most learning methods are suitable only for certain data types. For the hybrid dataset consists of nominal and numeric attributes, to apply the learning algorithms, some attributes must be transformed into the appropriate types. This procedure could damage the nature of dataset. We propose a model tree approach to integrate several characteristically different learning algorithms to solve the classification problem. We employ the decision tree as the classification framework and incorporate support vector machines into the tree construction process. This design removes the discretization procedure usually necessary for tree construction and provides the powerful multivariate decisions. Experiments show that our purposed method has better performance than that of other competing methods. 1

with different numbers of records of learning data set was performed. The error of classification with the cross - validation method is determined. Bibliography

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