Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from Continuous Wavelet Transform: a real-case Application
A genetic algorithm (GA) based feature subset
selection algorithm is proposed in which the correlation structure of
the features is exploited. The subset of features is validated according
to the classification performance. Features derived from the
continuous wavelet transform are potentially strongly correlated.
GA-s that do not take the correlation structure of features into
account are inefficient. The proposed algorithm forms clusters of
correlated features and searches for a good candidate set of clusters.
Secondly a search within the clusters is performed. Different
simulations of the algorithm on a real-case data set with strong
correlations between features show the increased classification
performance. Comparison is performed with a standard GA without
use of the correlation structure.
[1] S. Raudys, and V. Pikelis, "On dimensionality, sample size,
classification error, and complexity of classification algorithm in pattern
recognition," IEEE Trans. on Pattern Analysis and Machine
Intelligence, vol. PAMI-2, no. 3, pp. 242-252, May 1980.
[2] S. Raudys, and K. Jain, "Small sample size effects in statistical pattern
recognition: recommendations for practitioners," IEEE Trans. on Pattern
Analysis and Machine Intelligence, vol. 13, no. 3, pp. 252-264, March
1991.
[3] I. Daubechies, Ten Lectures On Wavelets, CBMS Regional Conference
Series in Applied Mathematics # 61, SIAM, 1992.
[4] G. Van Dijck, M. Wevers, and M. Van Hulle, "Corrosion time series
classification using the continuous wavelet transform and MML density
estimation," submitted for publication, International Conference on
Computational Intelligence, ICCI 2004.
[5] G. John, R. Kohavi, and K. Pfleger, "Irrelevant features and the subset
selection problem," in Machine Learning: Proc. of the Eleventh Int.
Conf., Morgan Kauffman, 1994, pp. 121-129.
[6] R. Kohavi, and G. John, "Wrappers for feature subset selection,"
Artificial Intelligence, vol. 97, spec. issue on relevance, pp. 273-324,
Dec. 1997.
[7] L. Citi, R. Poli, and F. Sepulveda, "An evolutionary approach to feature
selection and classification in P300-based BCI," in Proc. of the 2nd Int.
Brain-Computer Interface Workshop and Training Course, Graz, 2004
[8] E. Kalapanidas, and N. Avouris, "Feature selection using a genetic
algorithm applied on an air quality forecasting problem," AI
communications, vol. 16, no. 4, pp. 235-251, 2003.
[9] J. Yang and V. Honavar, "Feature subset selection using a genetic
algorithm," IEEE Intelligent Systems, vol. 13, nr. 2, pp. 44-49, 1998.
[10] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution
Programs, Springer-Verlag, 3rd edition, 1999.
[11] M. A.T. Figueirido, and A.K. Jain, "Unsupervised learning of finite
mixture models," IEEE Trans. On Pattern Analysis and Machine
Intelligence, vol. 24, no. 3, pp.381-396, March 2002.
[12] R. O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, 2nd ed.,
Wiley-Interscience, 2000, pp. 550-559.
[13] G. W. Milligan, and M.C. Cooper, "An examination of procedures for
detecting the number of clusters in a data set," Psychometrika 50(2), pp.
159-179, 1985.
[1] S. Raudys, and V. Pikelis, "On dimensionality, sample size,
classification error, and complexity of classification algorithm in pattern
recognition," IEEE Trans. on Pattern Analysis and Machine
Intelligence, vol. PAMI-2, no. 3, pp. 242-252, May 1980.
[2] S. Raudys, and K. Jain, "Small sample size effects in statistical pattern
recognition: recommendations for practitioners," IEEE Trans. on Pattern
Analysis and Machine Intelligence, vol. 13, no. 3, pp. 252-264, March
1991.
[3] I. Daubechies, Ten Lectures On Wavelets, CBMS Regional Conference
Series in Applied Mathematics # 61, SIAM, 1992.
[4] G. Van Dijck, M. Wevers, and M. Van Hulle, "Corrosion time series
classification using the continuous wavelet transform and MML density
estimation," submitted for publication, International Conference on
Computational Intelligence, ICCI 2004.
[5] G. John, R. Kohavi, and K. Pfleger, "Irrelevant features and the subset
selection problem," in Machine Learning: Proc. of the Eleventh Int.
Conf., Morgan Kauffman, 1994, pp. 121-129.
[6] R. Kohavi, and G. John, "Wrappers for feature subset selection,"
Artificial Intelligence, vol. 97, spec. issue on relevance, pp. 273-324,
Dec. 1997.
[7] L. Citi, R. Poli, and F. Sepulveda, "An evolutionary approach to feature
selection and classification in P300-based BCI," in Proc. of the 2nd Int.
Brain-Computer Interface Workshop and Training Course, Graz, 2004
[8] E. Kalapanidas, and N. Avouris, "Feature selection using a genetic
algorithm applied on an air quality forecasting problem," AI
communications, vol. 16, no. 4, pp. 235-251, 2003.
[9] J. Yang and V. Honavar, "Feature subset selection using a genetic
algorithm," IEEE Intelligent Systems, vol. 13, nr. 2, pp. 44-49, 1998.
[10] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution
Programs, Springer-Verlag, 3rd edition, 1999.
[11] M. A.T. Figueirido, and A.K. Jain, "Unsupervised learning of finite
mixture models," IEEE Trans. On Pattern Analysis and Machine
Intelligence, vol. 24, no. 3, pp.381-396, March 2002.
[12] R. O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, 2nd ed.,
Wiley-Interscience, 2000, pp. 550-559.
[13] G. W. Milligan, and M.C. Cooper, "An examination of procedures for
detecting the number of clusters in a data set," Psychometrika 50(2), pp.
159-179, 1985.
@article{"International Journal of Information, Control and Computer Sciences:52875", author = "G. Van Dijck and M. M. Van Hulle and M. Wevers", title = "Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from Continuous Wavelet Transform: a real-case Application", abstract = "A genetic algorithm (GA) based feature subset
selection algorithm is proposed in which the correlation structure of
the features is exploited. The subset of features is validated according
to the classification performance. Features derived from the
continuous wavelet transform are potentially strongly correlated.
GA-s that do not take the correlation structure of features into
account are inefficient. The proposed algorithm forms clusters of
correlated features and searches for a good candidate set of clusters.
Secondly a search within the clusters is performed. Different
simulations of the algorithm on a real-case data set with strong
correlations between features show the increased classification
performance. Comparison is performed with a standard GA without
use of the correlation structure.", keywords = "Classification, genetic algorithm, hierarchicalagglomerative clustering, wavelet transform.", volume = "1", number = "8", pages = "2377-5", }