Abstract
This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for feature selection. The importance of feature is ranked by evaluating the sensitivity of the network output to the feature input in terms of the partial derivative. A systematic approach to remove irrelevant features based on the sensitivity is developed. Five futures contracts are examined in the experiment. Based on the simulation results, it is shown that that saliency analysis is effective in SVMs for identifying important features.
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References
Muller, R., Smola, J. A., Scholkopf, B.: Prediction Time Series with Support Vector Machines. In Proceedings of International Conference on Artificial Neural Networks (1997) 999
Vapnik, V. N., Golowich, S. E., Smola, A. J.: Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. Advances in Neural Information Processing Systems 9 (1996) 281–287
Barzilay, O., Brailovsky, V. L.: On Domain Knowledge and Feature Selection Using a SVM. Pattern Recognition Letters 20 (1999) 475–484.
Steppe, J. M., Bauer, Jr. K.W.: Feature Saliency Measures. Computers Math. Application 33 (1997) 109–126
Reed, R.: Pruning algorithms-a survey. IEEE Transactions on Neural Networks 4 (1993) 940–947
Ruck, D. W., Rogers, S. K., Kabrisky, M.: Feature Selection Using a Multi-layer Perceptron. Journal of Neural Network Computing 2 (1990) 40–48
Vapnik, V. N.: The Nature of Statistical Learning Theory. Springer-Verlag, New York 1995
Zurada, J. M., Malinowski, A., Usui, S.: Perturbation Method for Deleting Redundant Inputs of Perceptron Networks, Neurocomputing 14 (1997) 177–193
Thomason, M.: The Practitioner Methods and Tool. Journal of Computational Intelligence in Finance 7(3) (1999) 36–45
Smola, A. J., Scholkopf, B.: A tutorial on Support Vector Regression. NeuroCOLT Technical Report TR, Royal Holloway College, London, UK 1998
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© 2000 Springer-Verlag Berlin Heidelberg
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Cao, L.J., Tay, F.E.H. (2000). Feature Selection for Support Vector Machines in Financial Time Series Forecasting. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_38
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DOI: https://doi.org/10.1007/3-540-44491-2_38
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