C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining and KDD: promise and challenges
Future Generation Computer Systems - Special double issue on data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Feature Extraction Based on Decision Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Comprehensible Interpretation of Relief's Estimates
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Building multi-way decision trees with numerical attributes
Information Sciences: an International Journal
IEEE Intelligent Systems
Expert Systems with Applications: An International Journal
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The decision-tree (DT) algorithm is a very popular and efficient data-mining technique. It is non-parametric and computationally fast. Besides forming interpretable classification rules, it can select features on its own. In this article, the feature-selection ability of DT and the impacts of feature-selection/extraction on DT with different training sample sizes were studied by using AVIRIS hyperspcetral data. DT was compared with three other feature-selection methods; the results indicated that DT was an unstable feature selector, and the number of features selected by DT was strongly related to the sample size. Trees derived with and without feature-selection/extraction were compared. It was demonstrated that the impacts of feature selection on DT were shown mainly as a significant increase in the number of tree nodes (14.13-23.81%) and moderate increase in tree accuracy (3.5-4.8%). Feature extraction, like Non-parametric Weighted Feature Extraction (NWFE) and Decision Boundary Feature Extraction (DBFE), could enhance tree accuracy more obviously (4.78-6.15%) and meanwhile a decrease in the number of tree nodes (6.89-16.81%). When the training sample size was small, feature-selection/extraction could increase the accuracy more dramatically (6.90-15.66%) without increasing tree nodes.