Abstract
When selecting features for knowledge discovery applications, stability is a highly desired property. By stability of feature selection, here it means that the feature selection outcomes vary only insignificantly if the respective data change slightly. Several stable feature selection methods have been proposed, but only with empirical evaluation of the stability. In this paper, we aim at providing a try to give an analysis for the stability of our ensemble feature weighting algorithm. As an example, a feature weighting method based on L2-regularized logistic loss and its ensembles using linear aggregation is introduced. Moreover, the detailed analysis for uniform stability and rotation invariance of the ensemble feature weighting method is presented. Additionally, some experiments were conducted using real-world microarray data sets. Results show that the proposed ensemble feature weighting methods preserved stability property while performing satisfactory classification. In most cases, at least one of them actually provided better or similar tradeoff between stability and classification when compared with other methods designed for boosting the stability.
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References
Ng, A.Y.: Feature selection, l1 vs. l2 regularization, and rotational invariance. In: Proceedings of International Conference on Machine Learning, Banff, Canada (2004)
Zhao, Z.: Spectral Feature Selection for Mining Ultrahigh Dimensional Data. PhD thesis, Arizona State University (2010)
Inza, I., Larranaga, P., Blanco, R., Cerrolaza, A.J.: Filter versus wrapper gene selection approaches in dna microarray domains. Artificial Intelligence in Medicine 31, 91–103 (2004)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research 3, 1289–1305 (2003)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowledge and Data Engineering 17, 494–502 (2005)
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Feature Extraction, Foundations and Applications. Springer, Physica-Verlag, New York (2006)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 31, 1157–1182 (2003)
Saeys, Y., Abeel, T., Van de Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 313–325. Springer, Heidelberg (2008)
Han, Y., Yu, L.: A variance reduction for stable feature selection. In: Proceedings of the International Conference on Data Mining, pp. 206–215 (2010)
Loscalzo, S., Yu, L., Ding, C.: Consensus group stable feature selection. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 567–575 (2009)
Abeel, T., Helleputte, T., Van de Peer, Y., Dupont, P., Saeys, Y.: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26, 392–398 (2010)
Yu, L., Han, Y., Berens, M.E.: Stable gene selection from microarray data via sample weighting. IEEE/ACM Trans. Computational Biology and Bioinformatics 9, 262–272 (2012)
Bousquet, O., Elisseeff, A.: Stability and generalization. Journal of Machine Learning Research 2, 499–526 (2002)
Elisseeff, A., Evgeniou, T., Pontil, M.: Stability of randomized learning algorithm. Journal of Machine Learning Research 6, 55–79 (2005)
Li, Y., Gao, S.Y., Chen, S.C.: Ensemble feature weighting based on local learning and diversity. In: AAAI Conference on Artificial Intelligence, pp. 1019–1025 (2012)
Woznica, A., Nguyen, P., Kalousis, A.: Model mining for robust feature selection. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 913–921 (2012)
Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)
Xu, H., Caramanis, C., Mannor, S.: Sparse algorithm are not stable: A no-free-lunch theorem. IEEE Trans. Pattern Analysis and Machine Intelligence 34, 187–193 (2012)
Crammer, K., Bachrach, R.G., Navot, A., Tishby, N.: Margin analysis of the lvq algorithm. In: Advances in Neural Information Processing Systems, pp. 462–469 (2002)
Sun, Y.J., Todorovic, S., Goodison, S.: Local learning based feature selection for high dimensional data analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 32, 1–18 (2010)
Schapire, R.E., Freud, Y., Bartlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26, 1651–1686 (1998)
Breiman, L.: Bagging predictors. Machine Learning 26, 123–140 (1996)
Agarwal, S., Niyogi, P.: Generalization bounds for ranking algorithm via algorithmic stability. Journal of Machine Learning Research 10, 441–474 (2009)
Anthony, M., Bartlett, P.L.: Neural Network Learning: Theoretical Foundations. Cambridge University Press, Cambridge (1999)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon cancer tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America, 6745–6750 (1999)
Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S.: Classification of human lung carcinomas by mrna expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences of the United States of America 98, 13790–13795 (2001)
Li, Y., Lu, B.L.: Feature selection based on loss margin of nearest neighbor classification. Pattern Recognition 42, 1914–1921 (2009)
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Li, Y., Huang, S., Chen, S., Si, J. (2013). Stable L2-Regularized Ensemble Feature Weighting. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_15
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DOI: https://doi.org/10.1007/978-3-642-38067-9_15
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