Feature Selection Based on Information Theory Filters
Włodzisław Duch, Jacek Biesiada, Tomasz Winiarski, Karol Grudziński, Krzysztof Grąbczewski · 2003
Feature selection is an essential component in all data mining applications. Ranking of futures was made by several inexpensive methods based on information theory. Accuracy of neural, similarity based and decision tree classifiers calculated with reduced number of features. Comparison with computationally more expensive feature elimination methods was made. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.