Abstract
The target of machine learning is a predictive model that performs well on unseen data. Often, such a model has multiple intended uses, related to different points in the tradeoff between (e.g.) sensitivity and specificity. Moreover, when feature selection is required, different feature subsets will suit different target performance characteristics. Given a feature selection task with such multiple distinct requirements, one is in fact faced with a very-many-objective optimization task, whose target is a Pareto surface of feature subsets, each specialized for (e.g.) a different sensitivity/specificity tradeoff profile. We argue that this view has many advantages. We motivate, develop and test such an approach. We show that it can be achieved successfully using a dominance-based multiobjective algorithm, despite an arbitrarily large number of objectives.
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References
Asuncion, A., Newman, D.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
Emmanouilidis, C.: Evolutionary multi-objective feature selection and ROC analysis with application to industrial machinery fault diagnosis. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailiou, K., Fogarty, T. (eds.) Evolutionary Methods for Design, Optimisation and Control. CIMNE (2002)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006)
Hughes, E.J.: Evolutionary many-objective optimisation: Many once or one many? In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), vol. 1, pp. 222–227. IEEE Service Center, Los Alamitos (2005)
Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In: Proceedings of the 16th International Conference on Pattern Recognition (ICPR 2002), vol. 1, pp. 568–571. IEEE Computer Society, Los Alamitos (2002)
Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition. International Journal of Pattern Recognition and Artificial Intelligence 17(6), 903–929 (2003)
Pappa, G.L., Freitas, A.A., Kaestner, C.A.A.: A multiobjective genetic algorithm for attribute selection. In: Proceedings of the 4th International Conference on Recent Advances in Soft Computing (RASC 2002), pp. 116–121 (2002)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Reynolds, A.P., Corne, D.W., Chantler, M.J. (2010). Feature Selection for Multi-purpose Predictive Models: A Many-Objective Task. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_39
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DOI: https://doi.org/10.1007/978-3-642-15844-5_39
Publisher Name: Springer, Berlin, Heidelberg
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