Feature selection based on the training set manipulation

  title={Feature selection based on the training set manipulation},
  author={Pavel Kr{\'i}zek and Josef Kittler and V{\'a}clav Hlav{\'a}c},
  journal={18th International Conference on Pattern Recognition (ICPR'06)},
A novel filter feature selection technique is introduced. The method exploits the information conveyed by the evolution of the training samples weights similarly to the Adaboost algorithm. Features are selected on the basis of their individual merit using a simple error function. The weights dynamics and its effect on the error function are utilised to identify and remove redundant and irrelevant features. In experiments we show that the performance of commonly employed learning algorithms… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper


Publications referenced by this paper.
Showing 1-10 of 10 references

Neural Networks for Pattern Recognition

C. M. Bishop
View 4 Excerpts
Highly Influenced

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery • 1998
View 2 Excerpts

The max-min approach to feature selection: its foundations and practical potential

P. Pudil, J. J. Novovičová, J. Kittler
Indian Journal of Pure and Applied Mathematics, • 1994
View 1 Excerpt

Pattern Recognition: A Statistical Approach

P. A. Devijver, J. Kittler
View 3 Excerpts

On the Possible Orderings in the Measurement Selection Problem

IEEE Transactions on Systems, Man, and Cybernetics • 1977
View 2 Excerpts

Similar Papers

Loading similar papers…