Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Selecting discrete and continuous features based on neighborhood decision error minimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector-based feature selection using Fisher's linear discriminant and Support Vector Machine
Expert Systems with Applications: An International Journal
Designing simulated annealing and subtractive clustering based fuzzy classifier
Applied Soft Computing
Feature selection for high-dimensional imbalanced data
Neurocomputing
Hi-index | 0.00 |
A new efficient unsupervised feature selection method is proposed to handle nominal data without data transformation. The proposed feature selection method introduces a new data distribution factor to select appropriate clusters. The proposed method combines the compactness and separation together with a newly introduced concept of singleton item. This new feature selection method considers all features globally. It is computationally inexpensive and able to deliver very promising results. Eight datasets from the University of California Irvine (UCI) machine learning repository and a high-dimensional cDNA dataset are used in this paper. The obtained results show that the proposed method is very efficient and able to deliver very reliable results.