Abstract
Class overlapping and small sample class sizes, a situation not infrequent in practical settings, can make very difficult the successful construction of a classification system. In this paper we will address this question by means of a new procedure, which we call Nonlinear Discriminant Analysis (NLDA), for classifier construction in such cases and that combines the excellent approximation properties of the well known Multilayer Perceptrons with the target-free classical discrimination technique of Fisher's Analysis. Besides a short description of NLDA fundamentals, we will give an illustration of its use in a practical problem, the assessment of professional performance of insurance salespersons.
J. Dorronsoro and C. Santa Cruz were partially supported by Spain's CICyT under grant TIC 98-0247. Both are also members of the Department of Computer Engineering, Universidad Autónoma de Madrid.
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© 1999 Springer-Verlag Berlin Heidelberg
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Aguado, D., Dorronsoro, J.R., Lucía, B., Santa Cruz, C. (1999). Small sample discrimination and professional performance assessment. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100523
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DOI: https://doi.org/10.1007/BFb0100523
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