Error-correcting output codes (ECOCs) represent classes with a set of output bits, where each bit encodes a binary classiication task corresponding to a unique partition of the classes. Algorithms that use ECOCs learn the function corresponding to each bit, and combine them to generate class predictions. ECOCs can reduce both variance and bias errors for multiclass classiication tasks when the ...

We present an approach to design of fault tolerant computing systems. In this paper, a technique is employed that enable the combination of several codes, in order to obtain flexibility in the design of error correcting codes. Code combining techniques are very effective, which one of these codes are turbo codes. The Algorithm-based fault tolerance techniques that to detect errors rely on the c...

In this paper, a multiclass classification problem is solved using multiple complementary neural networks. Two techniques are applied to multiple complementary neural networks which are one-against-all and error correcting output codes. We experiment our proposed techniques using an extremely imbalance data set named glass from the UCI machine learning repository. It is found that the combinati...

Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. Lately, the ECOC framework was extended from the binary to the ternary case to allow classes to be ignored by a certain classifier, allowing in this way to increase the number of possible dichotomies to be selected. Nevertheless, the effect of the zero symbol by which d...

0167-8655/$ see front matter 2011 Published by doi:10.1016/j.patrec.2011.09.023 ⇑ Corresponding author at: Centre de Visió per Com O, 08193 Bellaterra, Barcelona, Spain. E-mail addresses: [email protected] (M.Á. B (S. Escalera), [email protected] (X. Baró), petia@maia maia.uab.es (J. Vitriá), [email protected] (O. Pujol). The classification of large number of object categories is a challenging t...

Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. In this paper, we propose a novel technique called ECOC-ONE (Optimal Node Embedding) to improve an initial ECOC configuration defining a strategy to create new dichotomies and improve optimally the performance. The process of searching for new dichotomies is guided by t...

Error Correcting Output Codes technique (ECOC) represents a general framework capable to extend any binary classification process to the multi-class case. In this work, we present a novel decoding strategy that takes advantage of the ECOC coding to outperform the up to now existing decoding strategies. The results show that the presented methodology considerably increases the performance of the...