We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analyzing the decision trees evolved we can remove the unessential parts, called introns, from the discovered decision trees. Since the resulting trees contain only useful information they are smaller and easier to understand. Moreover, ...

Trees are a valuable way of displaying structure in datasets, especially for classification problems. Improved classification results can be achieved using forests of trees. Adding various visualization methods and interactive tools for analysis of individual trees and of whole forests gives complementary insight into the data. This paper describes different views and methods to analyze tree fo...

When different subsamples of the same data set are used to induce classification trees, the structure of the built classifiers is very different. The stability of the structure of the tree is of capital importance in many domains, such as illness diagnosis, fraud detection in different fields, customer’s behaviour analysis (marketing), etc, where comprehensibility of the classifier is necessary...

Classification trees are one of the most popular types of classifiers, with ease of implementation and interpretation being among their attractive features. Despite the widespread use of classification trees, theoretical analysis of their performance is scarce. In this paper, we show that a new family of classification trees, called dyadic classification trees (DCTs), are near optimal (in a min...

OBJECTIVE Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine-...

We introduce the powerful, flexible and efficient nonparametric Classification and Regression Trees (CART) algorithm to the analysis of mortgage default data while conducting the first academic study of mortgage default in Israel. CART’s strengths in dealing with large data sets, high dimensionality, mixed data types, missing data, different relationships between variables in different parts of...

Classification trees are widely used in the data mining community. Typically, trees are constructed to try and maximize their mean classification accuracy. In this paper, we propose an alternative to using the mean accuracy as the performance measure of a tree. We investigate the use of various percentiles (representing the risk aversion of a decision maker) of the distribution of classificatio...

Using satellite imagery for the study of Earth's resources is attended by manyresearchers. In fact, the various phenomena have different spectral response inelectromagnetic radiation. One major application of satellite data is the classification ofland cover. In recent years, a number of classification algorithms have been developed forclassification of remote sensing data. One of the most nota...

In this paper we challenge three of the underlying principles of CART, a well know approach to the construction of classification and regression trees. Our primary concern is with the penalization strategy employed to prune back an initial, overgrown tree. We reason, based on both intuitive and theoretical arguments, that the pruning rule for classification should be different from that used fo...