Abstract
An algorithm that identifies the variable subsets with most discriminatory power (in a predictive sense) is proposed. This algorithm minimizes parametric estimates of the error rate among all the possible variable subsets, evaluating only a fraction of the total number of subsets. The computational feasibility is illustrated by simulation experiments.
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References
Furnival, G.M. 1971. All Possible Regressions with Less Computation. Technometrics, 13: 403–408.
Furnival, G.M. & Wilson, R.W. 1974. Regressions by Leaps and Bounds. Technometrics, 16: 499–511.
Huberty, C.J. 1994. Applied Discriminant Analysis, New York, NY: Wiley.
Huberty, C.J. & Wisenbaker, J.M. 1992. Variable Importance in Multivariate Group Comparisons. Journal of Educational Statistics, 17: 75–91.
Jain, A.K. & Waller, W.G. 1978. On the Optimal Number of Features in the Classification of Multivariate Gaussian Data. Pattern Recognition, 10: 365–374.
Lachenbruch, P A. 1968. On Expected Probabilities of Misclassification in Discriminant Analysis, Necessary Sample Size, and a Relation with the Multiple Correlation Coefficient. Biometrics, 24: 823–834.
McCabe, G.P. 1975. Computations for Variable Selection in Discriminant Analysis. Technometrics, 17: 103–109.
McKay, R.J. & Campbell, N.A. 1982a. Variable Selection Techniques in Discriminant Analysis I. Description. British Journal of Mathematical and Statistical Psychology, 3 5: 1–29.
McKay, R.J. & Campbell, N.A. 1982b. Variable Selection Techniques in Discriminant Analysis II. Allocation. British Journal of Mathematical and Statistical Psychology, 35: 30–41.
McLachlan, G. J. 1974. An Asymptotic Unbiased Technique for Estimating the Error Rates in Discriminant Analysis. Biometrics, 30: 239–249.
McLachlan, G. J. 1992. Discriminant Analysis and Statistical Pattern Recognition, New York, NY: Wiley.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Duarte Silva, A.P. (1998). A “Leaps and Bounds” Algorithm for Variable Selection in Two-Group Discriminant Analysis. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_31
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DOI: https://doi.org/10.1007/978-3-642-72253-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
Online ISBN: 978-3-642-72253-0
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