Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification

Authors:
Sang-Hyeun Park;Johannes Fürnkranz
Affiliations:
TU Darmstadt, Knowledge Engineering Group, Darmstadt, Germany D-64289;TU Darmstadt, Knowledge Engineering Group, Darmstadt, Germany D-64289
Venue:
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Year:
2009

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Abstract

We present an adaptive decoding algorithm for ternary ECOC matrices which reduces the number of needed classifier evaluations for multiclass classification. The resulting predictions are guaranteed to be equivalent with the original decoding strategy except for ambiguous final predictions. The technique works for Hamming Decoding and several commonly used alternative decoding strategies. We show its effectiveness in an extensive empirical evaluation considering various code design types: Nearly in all cases, a considerable reduction is possible. We also show that the performance gain depends on the sparsity and the dimension of the ECOC coding matrix.