Abstract
This paper presents an overview of several methods that can be used to improve recognition of a weak class in binary classification problem. We illustrated this problem in the context of data mining based on a biological population data. We analyze feasibility of several approaches such as boosting, non-symmetric cost of misclassification events, and combining several weak classifiers (metalearning). We show that metalearning seems counter-productive if the goal is to enhance the recognition of a weak class, and that the method of choice would consist in combining boosting with the non-symmetric cost approach.
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Maciejewski, H., Walkowicz, E., Unold, O., Skrobanek, P. (2012). Enhancing Recognition of a Weak Class – Comparative Study Based on Biological Population Data Mining. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_16
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DOI: https://doi.org/10.1007/978-3-642-29350-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
Online ISBN: 978-3-642-29350-4
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