International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Machine Learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Empirical characterization of random forest variable importance measures
Computational Statistics & Data Analysis
CART algorithm for spatial data: Application to environmental and ecological data
Computational Statistics & Data Analysis
Binary trees for dissimilarity data
Computational Statistics & Data Analysis
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It has been recognized that Classification trees (CART) are unstable; a small perturbation in the input variables or a fresh sample can lead to a very different classification tree. Some approaches exist that try to correct this instability. However, their benefits can, at present, be appreciated only qualitatively. A similarity measure between two classification trees is introduced that can measure their closeness. Its usefulness is illustrated with synthetic data on the impact of radioactivity deposit through the environment. In this context, a modified node level stabilizing technique, referred to as the NLS-REP method, is introduced and shown to be more stable than the classical CART method.