A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Technical section: Second order 3D shape features: An exhaustive study
Computers and Graphics
Feature selection by maximum marginal diversity: optimality and implications for visual recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Increasing classification robustness with adaptive features
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
| Hi-index | 0.00 |
The quality of a retrieval system relies to major part on the quality of the used features. The features have to be small and compact, but also discriminative. Feature selection is one way to achieve both goals. We present a new feature selection method with the focus on retrieval purposes. The new method is based on the well known Relief algorithm. The new algorithm is shown to be superior to state-of-the-art methods both on toy problems and real-life 3D-Shape and image retrieval tasks. The algorithm is based on the intuition that distances to false detection has to be enlarged and distances to non-detected positives has to be shortened.