Solving Multiclass Learning Problems via Error-Correcting Output Codes

@article{Dietterich1995SolvingML,
  title={Solving Multiclass Learning Problems via Error-Correcting Output Codes},
  author={Thomas G. Dietterich and Ghulum Bakiri},
  journal={ArXiv},
  year={1995},
  volume={cs.AI/9501101}
}
Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by studying collections of training examples of the form (xi, f(xi)). Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary… Expand
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2,822 Citations

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This is the first work that comprehensively studies consistency properties of output code based methods for multiclass learning, and derives general conditions on the binary surrogate loss under which the one-vs-all and all-pairs code matrices yield consistent algorithms with respect to the multiclass 0-1 loss. Expand
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This paper describes a new technique for multiclass learning problems by combining Freund and Schapire's boosting algorithm with the main ideas of Diet- terich and Bakiri's method of error-correcting output codes (ECOC), and shows that the new hybrid method has advantages of both. Expand
Stochastic Organization of Output Codes in Multiclass Learning Problems
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evolutionary Design of Code-matrices for Multiclass Problems
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This chapter presents a survey on techniques for multiclass problems code-matrix design, and shows how evolutionary techniques can be employed to solve this problem. Expand
Active learning with error-correcting output codes
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A novel multi-class active learning algorithm to tackle the above problems and select the most informative instances, called active learning with error-correcting output codes (ECOCAL), which outperforms several state-of-the-art active learning methods on both binary and multi- class datasets. Expand
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
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A general method for combining the classifiers generated on the binary problems is proposed, and a general empirical multiclass loss bound is proved given the empirical loss of the individual binary learning algorithms. Expand
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This paper proposes a new multiclass boosting algorithm that modifies the coding matrix according to the learning ability of the base learner, and shows experimentally that this algorithm is very efficient in optimizing the multiclass margin cost, and outperforms existing multiclass algorithms such as AdaBoost. Expand
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ECC, that, by using a different weighting of the votes of the weak hypotheses, is able to improve on the performance of ADABOOST.OC, is arguably a more direct reduction of multiclass learning to binary learning problems than previous multiclass boosting algorithms. Expand
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This paper reformulate the ECOC models from the perspective of multi-task learning, where the binary classifiers are learned in a common subspace of data, and presents the kernel extension of the proposed model. Expand
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