Instance-Based Learning Algorithms
Machine Learning
Computer
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The use of features selection and nearest neighbors rule for faults diagnostic in induction motors
Engineering Applications of Artificial Intelligence
Combination of feature selection approaches with SVM in credit scoring
Expert Systems with Applications: An International Journal
Neural PCA and maximum likelihood hebbian learning on the GPU
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
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The technique of machinery fault diagnosis has been greatly enhanced over recent years with the application of many pattern classification methods. However, these classification methods suffer from the ''curse of dimensionality'' when applied to high-dimensional fault diagnosis data. In order to solve the problem, this paper proposes a hybrid model which combines multiple feature selection models to select the most significant input features from all potentially relevant features. Among the models, eight filter models are used to pre-rank the candidate features. They include data variance, Pearson correlation coefficient, the Relief algorithm, Fisher score, class separability, chi-squared, information gain and gain ratio. These variable ranking models measure features from various perspectives, and lead to different ranking results. Based on the effect of the ranking results on the Radial Basis Function (RBF) classification, a weighted voting scheme is then introduced to re-rank features. Furthermore, two wrapper models, a Binary Search (BS) model and a Sequential Backward Search (SBS) model are utilized to minimize the number of relevant features. To demonstrate the potential for applying the method to machinery fault diagnosis, two case studies are discussed. The experiment results support the conclusion that this method is useful for revealing fault-related frequency features.