The Community for Technology Leaders
Green Image
Issue No. 10 - Oct. (2018 vol. 40)
ISSN: 0162-8828
pp: 2388-2401
Miguel Angel Bautista Martin , Heidelberg University, Heidelberg, BW, Germany
Oriol Pujol , Autonomous University of Barcelona, Bellaterra (Cerdanyola), Barcelona, Spain
Fernando De la Torre , Carnegie Mellon University, Pittsburgh, PA
Sergio Escalera , Autonomous University of Barcelona, Bellaterra (Cerdanyola), Barcelona, Spain
Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method. Our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches.
Encoding, Support vector machines, Optimization, Electronic mail, Boosting, Market research

M. A. Bautista Martin, O. Pujol, F. D. Torre and S. Escalera, "Error-Correcting Factorization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 40, no. 10, pp. 2388-2401, 2018.
85 ms
(Ver 3.3 (11022016))