| Title:
|
A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers (English) |
| Author:
|
Kudo, Mineichi |
| Author:
|
Sklansky, Jack |
| Language:
|
English |
| Journal:
|
Kybernetika |
| ISSN:
|
0023-5954 |
| Volume:
|
34 |
| Issue:
|
4 |
| Year:
|
1998 |
| Pages:
|
[429]-434 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
Needs of feature selection in medium and large problems increases in many fields including medical and image processing fields. Previous comparative studies of feature selection algorithms are not satisfactory in problem size and in criterion function. In addition, no way has not shown to compare algorithms with different objectives. In this study, we propose a unified way to compare a large variety of algorithms. Our results show that the sequential floating algorithms promises for up to medium problems and genetic algorithms for medium and large problems. (English) |
| Keyword:
|
feature selection |
| Keyword:
|
pattern classifiers |
| MSC:
|
68T10 |
| MSC:
|
68U99 |
| idZBL:
|
Zbl 1274.68668 |
| . |
| Date available:
|
2009-09-24T19:18:31Z |
| Last updated:
|
2015-03-28 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/135227 |
| . |
| Reference:
|
[1] Ferri F. J., Pudil P., Hatef M., Kittler J.: 1994.Comparative study of techniques for large–scale feature selection. In: Pattern Recognition in Practice IV (E. S. Gelsema and L. N. Kanal, eds.), Elsevier Science B. V. 1994, pp. 403–413 |
| Reference:
|
[2] Foroutan I., Sklansky J.: Feature selection for automatic classification of non–gaussian data.IEEE. Trans. Systems Man Cybernet. 17 (1987), 187–198 10.1109/TSMC.1987.4309029 |
| Reference:
|
[3] Kittler J.: 1978.Feature set search algorithms. In: Pattern Recognition and Signal Processing (C. H. Chen, ed.), Sijthoff and Noordhoff, Alphen aan den Rijn 1978, pp. 41–60 |
| Reference:
|
[4] Murphy P. M., Aha D. W.: UCI Repository of machine learning databases [Machine–readable dta repository].Department of Information and Computation Science University of California, Irivne 1996 |
| Reference:
|
[5] Pudil P., Novovičová J., Kittler J.: Floating search methods in feature selection.Pattern Recognition Lett. 15 (1994), 1119–1125 10.1016/0167-8655(94)90127-9 |
| Reference:
|
[6] Siedlecki W., Sklansky J.: A note on genetic algorithms for large–scale feature selection.Pattern Recognition Lett. 10 (1989), 335–347 Zbl 0942.68690, 10.1016/0167-8655(89)90037-8 |
| Reference:
|
[7] Sklansky J., Siedlecki W.: Large–scale feature selection.In: Handbook of Pattern Recognition and Computer Vision (L. F. Pau, C. H. Chen and P. S. P. Wang, eds.), Chapter 1.3, World Scientific 1993, pp. 61–123 |
| Reference:
|
[8] Vriesenga M. R.: Genetic Selection and Neureal Modeling for Designing Pattern Classifier.Doctor Thesis, University of California, Irvine 1995 |
| Reference:
|
[9] Yu B., Yuan B.: A more efficient branch and bound algorithm for feature selection.Pattern Recognition 26 (1993), 6, 883–889 10.1016/0031-3203(93)90054-Z |
| Reference:
|
[10] Zongker D., Jain A.: Algorithms for feature selection: An evaluation.In: 13th International Conference on Pattern Recognition 1996, pp. 18–22 |
| . |
