Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some sub-trees which do not lessen the error rate can be relevant to point out some populations of specific interest or to give a representation of a large data file. We propose here a new pruning method (called pruning) which takes into account the complexity of sub-trees and which is able to ke...

The amalgamation of leaf-labelled trees into a single (super)tree that “displays” each of the input trees is an important problem in classification. We discuss various approaches to this problem and show that a simple and well known polynomial-time algorithm can be used to solve this problem whenever the input set of trees contains a minimum size subset that uniquely determines the supertree. O...

The normalised compression distance measures the mutual compressibility of two signals. We show that this distance can be used for classification on real images. Furthermore, the same compressor can also operate on derived features with no further modification. We consider derived features consisting of trees indicating the containment and relative area of connected sets within the image. It ha...

Motivation: Position-specific scoring matrices have been used extensively to recognize highly conserved protein regions. We present a method for accelerating these searches using a suffix tree data structure computed from the sequences to be searched. Methods: Building on earlier work that allows evaluation of a scoring matrix to be stopped early, the suffix tree-based method excludes many prot...

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms which also induces classification ru...

This paper reports on the experience in the applications of the classification-tree methodology to a real-life system. As a result of the study, we found the need to supplement the original method by introducing a new relation operator and a new technique for constructing classification trees. Our supplement helps to enforce the completeness of test cases and improve on the effectiveness of cla...

⎯In this paper we investigate the capability of the genetic programming approach for producing hierarchical, rule-based, classification trees. These trees can be seen as an extension to the machine learning decision trees concept, where the predicates here can be complex expressions rather than just simple attribute-value comparisons. In order to improve the search ability and to produce meanin...

We introduce a new method, called CS4, to construct committees of decision trees for classification. The method considers different top-ranked features as the root nodes of member trees. This idea is particularly suitable for dealing with high-dimensional bio-medical data as top-ranked features in this type of data usually possess similar merits for classification. To make a decision, the commi...

A novel method for classification of abnormality in anatomical tree structures is presented. A tree is classified based on direct comparisons with other trees in a dissimilarity-based classification scheme. The pair-wise dissimilarity measure between two trees is based on a linear assignment between the branch feature vectors representing those trees. Hereby, localized information in the branch...