Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
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Individual optimal feature selection algorithm is simple and effective. Generally, single criterion is chosen for feature selection. But in this case, it is possible that features with good performance on other criteria will be neglected, and causing a negative impact to feature selection result. For the problem, the paper proposes a comprehensive evaluation model based on fuzzy correlation projection for evaluating the comprehensive indexs of individual features, and takes the results as the basis of feature selection. Definition to the model and its application procedure are described. Finally, the proposed method is applied to evaluate and select the underwater target recognition feature. The experimental result shows that the selected feature subset based on comprehensive indexs has a higher testing recognition rate than that based on single criterion, e.g. Relief-F indexs. Therefore, the proposed individual feature evaluation and selection method based on comprehensive indexs is feasible and effective.