Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
How to solve it: modern heuristics
How to solve it: modern heuristics
Induction By Attribute Elimination
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Induction-algorithm-oriented feature elimination considers not only the data and the target concept but also the induction algorithm that will learn the target concept from the data. Because of its very nature, IAOFE is controlled by abundant parameters. This article reports on a study to understand which parameter settings can produce ideal performance from IAOFE. The authors ran comparative studies for various parameter settings and identified effective configurations. Empirical evidence from a large number of data sets demonstrates that IAOFE, with the suggested parameter configurations, can achieve higher predictive accuracy than existing popular feature selection approaches with statistically significant frequencies.