Hybrid genetic algorithm for feature selection with hyperspectral data

References

• Altidor, W., Khoshgoftaar, T.M. and Hulse, J.V, 2011, Robustness of filter-based feature ranking: a case study. In Proceedings of 24th Florida Artificial Intelligence Research Society Conference (FLAIRS-24), 18–20 May, Palm Beach, FL (Palo Alto, CA: AAAI Press), pp. 453–458.
   Google Scholar
• Bajcsy, P. and Groves, P., 2004, Methodology for hyperspectral band selection. Photogrammetric Engineering and Remote Sensing, 70, pp. 793–802.
   Web of Science ®Google Scholar
• Boser, B.E., Guyon, I.M. and Vapnik, V.N., 1992, Training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual Workshop on Computational Learning Theory, D. Haussler (Ed.), pp. 144–152 (Pittsburgh, PA: ACM Press).
   Google Scholar
• Casillas, J., Cordón, O., Del Jesus, M.J. and Herrera, F., 2001, Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Information Science, 136, pp. 135–157.
   Web of Science ®Google Scholar
• Chang, C.-I., 2007, Hyperspectral Data Exploitation: Theory and Applications (Hoboken, NJ: Wiley).
   Google Scholar
• Cortes, C. and Vapnik, V., 1995, Support-vector network. Machine Learning, 20, pp. 273–297.
   Web of Science ®Google Scholar
• Cover, T.M., 1974, The best two independent measurements are not the two best. IEEE Transactions on Systems, Man, and Cybernetics, SMC-4, pp. 116–117.
   Google Scholar
• Dash, M. and Liu, H., 1997, Feature selection for classification. Intelligent Data Analysis: An International Journal, 1, pp. 131–156.
   Google Scholar
• Foody, G.M., 2004, Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering and Remote Sensing, 70, pp. 627–633.
   Web of Science ®Google Scholar
• Foody, G.M. and Mathur, A., 2004, Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93, pp. 107–117.
   Web of Science ®Google Scholar
• Guyon, I. and Elisseeff, A., 2003, An introduction to variable and feature selection. Journal of Machine Learning Research, 3, pp. 1157–1182.
   Google Scholar
• Hart, W.E., Smith, J.E. and Krasnogor, N., 2005, Recent advances in memetic algorithms. In Studies in Fuzziness and Soft Computing, W.E. Hart, N. Krasnogor and J.E. Smith (Eds.), Vol. 166 (Berlin: Springer).
   Google Scholar
• Hastie, T. and Tibshirani, R., 1996, Discriminant adaptive nearest neighbour classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, pp. 607–615.
   Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R. and Friedman, J., 2009, The Elements of Statistical Learning – Data Mining, Inference, and Prediction (New York, NY: Springer).
   Google Scholar
• Hughes, G.F., 1968, On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, IT-14, pp. 55–63.
   Web of Science ®Google Scholar
• Jain, A. and Zongker, D., 1997, Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, pp. 153–158.
   Web of Science ®Google Scholar
• Kohavi, R. and John, G.H., 1997, Wrappers for feature subset selection. Artificial Intelligence, 97, pp. 273–324.
   Web of Science ®Google Scholar
• Kononenko, I., 1994, Estimating attributes: analysis and extensions of RELIEF. In European Conference on Machine Learning, F. Bergadano and L.D. Raedt (Eds.), pp. 171–182 (Berlin: Springer).
   Google Scholar
• Li, S., Wu, H., Wan, D. and Zhu, J., 2011, An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine. Knowledge-Based Systems, 24, pp. 40–48.
   Web of Science ®Google Scholar
• Luke, S., 2009, Essentials of Metaheuristics. Available online at http://cs.gmu.edu/~sean/book/metaheuristics/ (accessed 5 December 2012).
   Google Scholar
• Moscato, P. and Cotta, C., 2003, A gentle introduction to memetic algorithms. In Handbook of Metaheuristics, F. Glover and G.A. Kochenberger (Eds.), pp. 105–144 (Boston, MA: Kluwer).
   Google Scholar
• Pal, M., 2006, Support vector machine-based feature selection for land cover classification: a case study with DAIS hyperspectral data. International Journal of Remote Sensing, 27, pp. 2877–2894.
   Web of Science ®Google Scholar
• Pal, M., 2009, Margin based feature selection for hyperspectral data. International Journal of Applied Earth Observations and Geoinformation, 11, pp. 212–220.
   Web of Science ®Google Scholar
• Pal, M., 2011, Modified nearest neighbour classifier for hyperspectral data classification. International Journal of Remote Sensing, 32, pp. 9207–9217.
   Web of Science ®Google Scholar
• Pal, M., 2012, Multinomial logistic regression based feature selection for hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 14, pp. 214–220.
   Web of Science ®Google Scholar
• Pal, M. and Foody, G.M., 2010, Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 48, pp. 2297–2306.
   Web of Science ®Google Scholar
• Quinlan, J.R., 1993, C4.5 Programs for Machine Learning (San Mateo, CA: Morgan Kaufmann).
   Google Scholar
• Rogers, A. and Prugel-Bennett, A., 1999, Modelling the dynamics of a steady-state genetic algorithm. In Proceeding of Foundation of Genetic Algorithms, W. Banzhaf and C. R. Reeves (Eds.), pp. 57–68 (San Francisco, CA: Morgan Kaufmann).
   Google Scholar
• Serpico, S.B. and Bruzzone, L., 2001, A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 39, pp. 1360–1367.
   Web of Science ®Google Scholar
• Vapnik, V.N., 1995, The Nature of Statistical Learning Theory (New York, NY: Springer).
   Google Scholar
• Yang, J. and Honavar, V., 1998, Feature subset selection using a genetic algorithm. IEEE Intelligent Systems, 13, pp. 44–49.
   Web of Science ®Google Scholar
• Yu, S., De Backer, S. and Scheunders, P., 2002, Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery. Pattern Recognition Letters, 23, pp. 183–190.
   Web of Science ®Google Scholar
• Zhu, Z., Jia, S. and Ji, Z., 2010, Towards a memetic feature selection paradigm. IEEE Computational Intelligence Magazine, 5, pp. 41–53.
   Web of Science ®Google Scholar
• Zhu, Z., Ong, Y.S. and Dash, M., 2007, Wrapper-filter feature selection algorithm using a memetic framework. IEEE Transactions on Systems, Man and Cybernetics – Part B, 37, pp. 70–76.
   PubMed Web of Science ®Google Scholar