Selected recommendable papers covering Feature Selection or containing Feature Selection as important sub-topic (note that the selection aims at giving an overview of various views to FS and FS sub-topics and - being limited - can not cover all that can be considered important in the FS field):
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concise overview of statistical pattern recognition[1] Statistical Pattern Recognition: A Review. IEEE Trans. PAMI, 22(1):4–37, 2000. -
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concise overview of feature selection[2] Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Trans. on KDE, 17(4):491–502, 2005. -
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book on feature extraction as well as selection[3] I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, editors. Feature Extraction – Foundations and Applications, volume 207 of Studies in Fuzziness and Soft Comp. Physica, Springer, 2006. -
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seminal book on statistical pattern recognition with strong feature selection coverage[4] Pattern Recognition: A Statistical Approach. Prentice Hall, 1982. -
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paper establishing the key terms of feature selection wrappers and filters[5] Wrappers for Feature Subset Selection. Artif. Intell., 97(1-2):273–324, 1997. -
BIB [6] Advances in Statistical Feature Selection. In ICAPR '01, LNCS 2013, pages 425–434. Springer, 2001. -
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recent overview of feature selection with focus on sequential sub-optimal methods[7] Efficient Feature Subset Selection and Subset Size Optimization, pages 75–97. INTECH, 2010. -
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concise overview of text categorization with sections on feature selection in very-high dimensional feature space[8] Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1):1–47, 2002. -
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paper on the problem of feature selection over-fitting[9] Feature Over-Selection. In Proc. S+SSPR, volume LNCS 4109, pages 622–631. Springer, 2006. -
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paper on information measures in feature selection[10] A New Perspective for Information Theoretic Feature Selection. In Proc. AISTATS '09, volume 5 of JMLR: W&CP, pages 49–56, 2009. -
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paper on information measures in feature selection[11] Feature Selection Based on Mutual Inf.: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans. PAMI, 27(8):1226–1238, 2005. -
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novel method that enables wrapper-like feature selection in very-high-dimensional problems[12] Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems. UTIA TR No. 2295, Czech Academy of Sciences, 2011. -
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comparison of feature selection methods[13] Feature Selection Algorithms in Classif. Problems: An Experimental Evaluation. Optimiz. Methods and Software, 22(1):199–212, 2007. -
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comparison of feature selection methods[14] Comparison of Algorithms that Select Features for Pattern Classifiers. Pattern Recognition, 33(1):25–41, 2000. -
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comparison of feature selection methods[15] Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Trans. PAMI, 19(2):153–158, 1997. -
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extensive survey of feature selection stability evaluation measures and related techniques[16] Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality. IEEE Trans. on PAMI, 32(11):1921–1939, 2010. -
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paper on feature selection stability[17] Stability of Feature Selection Algorithms: A Study on High-Dimensional Spaces. Knowledge and Information Systems, 12(1):95–116, 2007. -
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paper on feature selection stability[18] A stability index for Feature Selection. In Proc. 25th IASTED International Multi-Conference AIAP'07, pages 390–395. ACTA Press, 2007.
List of papers covering FST related topics or papers reporting results obtained using FST (of all FST versions), sorted by date of publication:
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BIB [19] The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround. In ICPR 2010. IEEE Computer Society, 2010. -
BIB PDF [20] On the Over-Fitting Problem of Complex Feature Selection Methods. In Proc. 5th International Computer Engineering Conference, pages 12–17. Cairo University, 2009. -
BIB WWW [21] A New Measure of Feature Selection Algorithms' Stability. In Proc. IEEE International Conference on Data Mining Workshops, pages 382–387. IEEE Computer Society, 2009. -
BIB [22] Criteria Ensembles in Feature Selection. In Proc. MCS, LNCS 5519, pages 304–313. Springer, 2009. -
BIB PDF [23] Dynamic Oscillating Search Algorithm for Feature Selection. In ICPR 2008. IEEE Computer Society, 2008. -
BIB PDF WWW [24] Evaluating the Stability of Feature Selectors that Optimize Feature Subset Cardinality. In Proc. S+SSPR, LNCS 5342, pages 956–966, 2008. -
BIB [25] Oscillating Feature Subset Search Algorithm for Text Categorization. In Proc. S+SSPR, volume LNCS 4109, pages 578–587. Springer, 2006. -
BIB PDF [26] Flexible-Hybrid Sequential Floating Search in Statistical Feature Selection. In Proc. S+SSPR, LNCS 4109, pages 632–639. Springer, 2006. -
BIB WWW [27] Fast Branch & Bound Algorithms for Optimal Feature Selection. IEEE Trans. on PAMI, 26(7):900–912, 2004. -
BIB [28] Pattern Recognition and String Matching, chapter Recent Feature Selection Methods in Statistical Pattern Recognition, pages 565–616. Springer, 2002. -
BIB PDF [29] Feature Selection toolbox. Pattern Recognition, 35(12):2749–2759, 2002. -
BIB PDF [30] Oscillating Search Algorithms for Feature Selection. In ICPR 2000, volume 02, pages 406–409. IEEE Computer Society, 2000. -
BIB [31] Improving Statistical Measures of Feature Subsets by Conventional and Evolutionary Approaches. In Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition, LNCS 1876, pages 77–86. Springer, 2000. -
BIB PDF [32] Adaptive Floating Search Methods in Feature Selection. Pattern Recognition Letters, 20(11-13):1157–1163, 1999.
References from the introductory Feature Selection page:
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BIB [33] Knowing and Guessing. John Wiley and Sons, 1969. -
BIB [34] The Nature of Statistical Learning Theory. John Wiley, New York, 1998. -
BIB [35] Learning Support Vectors for Face Identification: Sensitivity to Registration Errors. In Wen-Hsiang Tsai, editor, Fourth Asian Conference on Computer Vision, pages 806–811. University of Taiwan, 2000. -
BIB PDF [36] Support Vector Machines for Face Authentication. In Tony P. Pridmore and Dave Elliman, editors, Proc. British Machine Vision Conference, BMVC '99, pages 543–552. British Machine Vision Association, 1999. -
BIB WWW [37] Novel Methods for Subset Selection with Respect to Problem Knowledge. IEEE Intelligent Systems, 13:66–74, 1998.