In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
CITATION STYLE
Ismailoglu, F., Sprinkhuizen-Kuyper, I. G., Smirnov, E., Escalera, S., & Peeters, R. (2015). Fractional programming weighted decoding for error-correcting output codes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9132, pp. 38–50). Springer Verlag. https://doi.org/10.1007/978-3-319-20248-8_4
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