Abstract
In this work we develop a new classification algorithm based on simulated annealing. The new method is evaluated and tested in a variety of situations which are generated and simulated by a Design of Experiments. This way, it is possible to find data characteristics that influence the relative classification performance of different classification methods. It turns out that the new method improves the classification performance of the classical Linear Discriminant Analysis (LDA) significantly in some situations. Moreover, in a real life example the new algorithm appears to be better than LDA.
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Luebke, K., Weihs, C. (2003). Testing a Simulated Annealing Algorithm in a Classification Problem. In: Albrecht, A., Steinhöfel, K. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2003. Lecture Notes in Computer Science, vol 2827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39816-5_6
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DOI: https://doi.org/10.1007/978-3-540-39816-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20103-8
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