Saliency Analysis of Support Vector Machines for Feature Selection in Financial Time Series Forecasting

This chapter 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. Two simulated non-linear time series and five real financial time series are examined in the experiment. The simulation results show that that saliency analysis is effective in SVMs for identifying important features.

[1]  Wright-Patterson Afb,et al.  Feature Selection Using a Multilayer Perceptron , 1990 .

[2]  Kenneth W. Bauer,et al.  Determining input features for multilayer perceptrons , 1995, Neurocomputing.

[3]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[4]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[5]  Kenneth W. Bauer,et al.  Feature saliency measures , 1997 .

[6]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[7]  Jacek M. Zurada,et al.  Perturbation method for deleting redundant inputs of perceptron networks , 1997, Neurocomputing.

[8]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[9]  William H. Murray,et al.  Microsoft C/C++ 7: The Complete Reference , 1992 .

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[12]  Victor L. Brailovsky,et al.  On domain knowledge and feature selection using a support vector machine , 1999, Pattern Recognit. Lett..

[13]  Patrick Gallinari,et al.  Variable selection with neural networks , 1996, Neurocomputing.