Abstract
In one-class classification problems all training examples belong to a single class. The absence of counter-examples represents a challenge to traditional Machine Learning and pre-processing techniques. This is the case of various feature selection techniques for labeled data. The selection of the most relevant features from a dataset usually benefits the performance obtained by classification algorithms. Despite the relevance of this issue, few techniques have been proposed for feature selection in one-class classification problems. Moreover, most of the existent techniques are wrapper approaches, which have to rely on a specific classification algorithm for feature selection, or aggregation techniques. This paper proposes a new filter feature selection approach for one-class classification. First, five feature selection measures from different paradigms are here employed or adapted to the one-class scenario. Next, the feature rankings produced by these measures are combined using different aggregation strategies. The proposed approach was able to reduce the size of the feature sets while maintaining or even improving the predictive performance obtained by the one-class classifier.
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AlcalÃ-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., Garca, S., Snchez, L., Herrera, F.: Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17(2-3), 255–287 (2011)
Bache, K., Lichman, M.: UCI machine learning repository (2014). http://archive.ics.uci.edu/ml
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. learn. 36(1-2), 105–139 (1999)
De Borda, J.C.: Mėmoire sur les ėlections au scrutin. Histoire de l’Acadėmie Royale des Sciences (1784)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–30 (2011)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–30 (2011)
Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised Learning, Chap. Graph-Based Methods. The MIT Press (2006)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press (2000)
Demṡar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Deris, S., Alashwal, H., Othman, M.: One-class support vector machines for protein-protein interactions prediction. Int. J. Biol. Med. Sci. 1(2), 120–127 (2006)
Dittman, D.J., Khoshgoftaar, T.M., Wald, R., Napolitano, A.: Classification performance of rank aggregation techniques for ensemble gene selection. In: The Twenty-Sixth International FLAIRS Conference (2013)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th international conference on World Wide Web, pp. 613–622. ACM (2001)
Elith*, J., H. Graham*, C., P. Anderson, R., Dud?k, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., J. Phillips, S., Richardson, K., Scachetti-Pereira, R., E. Schapire, R., Sober?n, J., Williams, S., S. Wisz, M., E. Zimmermann, N.: Novel methods improve prediction of species? distributions from occurrence data. Ecography 29 (2), 129–151 (2006). doi:10.1111/j.2006.0906-7590.04596.x
Hall, M.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings 17th International Conference Machine Learning, pp. 359–366 (2000)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009). doi:10.1145/1656274.1656278
He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: NIPS, Vol. 186, p. 189 (2005)
He, X., Niyogi, P.: Locality preserving projections. In: NIPS, Vol. 16, pp. 234–241 (2003)
Hoffmann, H.: Kernel pca for novelty detection. Patt. Recogn. 40(3), 863–874 (2007)
Jeong, Y.S., Kang, I.H., Jeong, M.K., Kong, D.: A new feature selection method for one-class classification problems. Systems, Man, and Cybernetics, Part C: Applications and Reviews. IEEE Trans. 42(6), 1500–1509 (2012)
Khan, S.S., Madden, M.G.: A survey of recent trends in one class classification. Artif. Intell. Cogn. Sci. 6206, 188–197 (2010)
Lian, H.: On feature selection with principal component analysis for one-class svm. Pattern Recogn. Lett. 33(9), 1027–1031 (2012)
Liu, H., Motoda, H.: Feature Extraction, Construction and Selection - A Data Mining Perspective. Kluwer Academic Publishers (1998)
Liu, H., Motoda, H., Setiono, R., Zhao, Z.: Feature selection : An ever evolving frontier in data mining. Knowl. Creat. Diffus. Utilization 4, 4–13 (2010)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)
Lorena, A.C., Jacintho, L.F.O., Siqueira, M.F., Giovanni, R., Lohmann, L.G., Carvalho, A.C.P.L.F., Yamamoto, M.: Comparing machine learning classifiers in potential distribution modelling. Expert Syst. Appl. 38, 5268–5275 (2011)
Lorena, L.H.N, De Carvalho, A.C.P.L.F., Lorena, A.C.: Seleo de atributos em problemas de classificao com uma nica classe [in portuguese]. In: X Encontro Nacional de Inteligncia Artificial e Computacional (ENIAC), pp. 1–11 (2013)
Mitra, P., Murthy, C.A., Pal, S.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)
Namsrai, E., Munkhdalai, T., Li, M., Shin, J.H., Namsrai, O.E., Ryu, K.H.: A feature selection-based ensemble method for arrhythmia classification. JIPS 9(1), 31–40 (2013)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering analysis and an algorithm. Proceedings of Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press 14, 849–856 (2001)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process. 99(0), 215–249 (2014)
Prati, R.C.: Combining feature ranking algorithms through rank aggregation. In: Neural Networks (IJCNN), The 2012 International Joint Conference on, pp. 1–8. IEEE (2012)
Reyes, J., Gilbert, D.: Combining one-class classification models based on diverse biological data for prediction of protein-protein interactions. In: Data Integration in the Life Sciences, Lecture Notes in Computer Science, Vol. 5109, pp. 177–191. Springer Berlin Heidelberg (2008)
Reyes, J.A., Gilbert, D.: Prediction of protein-protein interactions using one-class classification methods and integrating diverse data. J. Integr. Bioinforma. 4(3), 77 (2007)
Scholkopf, B., Plattz, J.C., Shawe-Taylory, J., Smolax, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Shahid, N., Aleem, S., Naqvi, I.H., Zaffar, N.: Support vector machine based fault detection & classification in smart grids. In: Globecom Workshops (GC Wkshps), 2012 IEEE, pp. 1526–1531. IEEE (2012)
Shen, Q., Diao, R., Su, P.: Feature selection ensemble. In: A. Voronkov (ed.) Turing-100, EPiC Series, Vol. 10, pp. 289–306. EasyChair (2012)
Shin, H.J., Eom, D.H., Kim, S.S.: One-class support vector machines-an application in machine fault detection and classification. Comput. Ind. Eng. 48(2), 395–408 (2005). doi:10.1016/j.cie.2005.01.009
Smart, E., Brown, D.J., Axel-Berg, L.: Comparing one and two class classification methods for multiple fault detection on an induction motor. In: ISIEA, 2013 IEEE Symposium on (2013)
Tax, D.M., Duin, R.P.: Characterizing one-class datasets. In: Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp. 21–26 (2005)
Tax, D.M.J.: One-class classification: Concept-learning in the absence of counter-examples. PhD dissertation, Delft University of Technology (2001)
Tsymbal, A., Cunningham, P.: Diversity in ensemble feature selection. Tech. rep., Department of Computer Science, Trinity College Dublin (2003). URL http://www.cs.tcd.ie/publications/tech-reports/reports.03/TCD-CS-2003-44.pdf
Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Diversity in search strategies for ensemble feature selection. Inf. fusion 6(1), 83–98 (2005)
Tsymbal, A., Puuronen, S., Patterson, D.W.: Ensemble feature selection with the simple bayesian classification. Information Fusion 4(2), 87–100 (2003)
Villalba, S.D., Cunningham, P.: An evaluation of dimension reduction techniques for one-class classification. Artif. Intell. Rev. 27(4), 273–294 (2007)
Wald, R., Khoshgoftaar, T.M., Dittman, D., Awada, W., Napolitano, A.: An extensive comparison of feature ranking aggregation techniques in bioinformatics. In: Information Reuse and Integration (IRI), 2012 IEEE 13th International Conference on, pp. 377–384. IEEE (2012)
Zhang, D., Wang, Y.: A new ensemble feature selection and its application to pattern classification. J. Control Theory Appl. 7(4), 419–426 (2009)
Zhao, Z., Liu, H.: Spectral feature selection for supervised and unsupervised learning. In: Proceedings 24th International Conference on Machine Learning, pp. 1151–1157 (2007)
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Lorena, L.H.N., Carvalho, A.C.P.L.F. & Lorena, A.C. Filter Feature Selection for One-Class Classification. J Intell Robot Syst 80 (Suppl 1), 227–243 (2015). https://doi.org/10.1007/s10846-014-0101-2
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DOI: https://doi.org/10.1007/s10846-014-0101-2