Abstract
It is known that the Error Correcting Output Code (ECOC) technique can improve generalisation for problems involving more than two classes. ECOC uses a strategy based on calculating distance to a class label in order to classify a pattern. However in some applications other kinds of information such as individual class probabilities can be useful. Least Squares(LS) is an alternative combination strategy to the standard distance based measure used in ECOC, but the effect of code specifications like the size of code or distance between labels has not been investigated in LS-ECOC framework. In this paper we consider constraints on choice of code matrix and express the relationship between final variance and local variance. Experiments on artificial and real data demonstrate that classification performance with LS can be comparable to the original distance based approach.
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Ghaderi, R., Windeatt, T. (2001). Least Squares and Estimation Measures via Error Correcting Output Code. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_15
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DOI: https://doi.org/10.1007/3-540-48219-9_15
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