@inproceedings{b6d65799933442609bbeaaa7f6b58575,
title = "Parsimonious feature extraction based on genetic algorithms and support vector machines",
abstract = "Most existing feature extraction algorithms aim at best preserving information in the original data or at improving the separability of data, but fail to consider the possibility of further reducing the number of used features. In this paper, we propose a parsimonious feature extraction algorithm. Its motivation is using as few features as possible to achieve the same or even better classification performance. It searches for the optimal features using a genetic algorithm and evaluates the features referring to Support Vector Machines. We tested the proposed algorithm by face recognition on the Yale and FERET databases. The experimental results proved its effectiveness and demonstrated that parsimoniousness should be a significant factor in developing efficient feature extraction algorithms.",
author = "Qijun Zhao and Hongtao Lu and Dapeng Zhang",
year = "2006",
month = jan,
day = "1",
doi = "10.1007/11759966_206",
language = "English",
isbn = "354034439X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1387--1393",
booktitle = "Advances in Neural Networks - ISNN 2006",
address = "Germany",
note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",
}