Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Orthogonal forward selection and backward elimination algorithms for feature subset selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Generalized iterative RELIEF for supervised distance metric learning
Pattern Recognition
| Hi-index | 0.00 |
The Relief is a popular feature selection algorithm. However, it is ineffective in removing redundant features due to its feature evaluation mechanism that all discriminative features are assigned with high relevance scores, regardless of the correlations in between. In the present study, we develop an orthogonal Relief algorithm (O-Relief) to tackle the redundant feature problem. The basic idea of the O-Relief algorithm is to introduce an orthogonal transform to decompose the correlation between features so that the relevance of a feature could be evaluated individually as it is done in the original Relief algorithm. Experiment results on four world problems show that the orthogonal Relief algorithm provides features leading to better classification results than the original Relief algorithm.