The Error-Correcting Output Codes (ECOC) is a representative approach of the binary ensemble classifiers for solving multi-class problems. There have been so many researches on an output coding method built on an ECOC foundation. In this paper, we revisit representative conventional ECOC methods in an overlapped learning viewpoint. For this purpose, we propose new OPC based output coding method...

This paper explores in detail the use of Error Correcting Output Coding (ECOC) for learning text classifiers. We show that the accuracy of a Naive Bayes Classifier over text classification tasks can be significantly improved by taking advantage of the error-correcting properties of the code. We also explore the use of different kinds of codes, namely Error-Correcting Codes, Random Codes, and Do...

Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Errorcorrecting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding.

Papers published in this report series are preliminary versions of journal articles and not for quotations. Abstract We show that error correcting output codes (ECOC) can further improve the eeects of error dependent adaptive resampling methods such as arc-lh. In traditional one-inn coding, the distance between two binary class labels is rather small, whereas ECOC are chosen to maximize this di...

We show that error correcting output codes (ECOC) can further improve the eeects of error dependent adaptive resampling methods such as arc-lh. In traditional one-inn coding, the distance between two binary class labels is rather small, whereas ECOC are chosen to maximize this distance. We compare one-inn and ECOC on a multiclass data set using standard MLPs and bagging and arcing voting commit...

Error-Correcting Output Codes (ECOC) is a general framework for combining binary classification in order to address the multi-class categorization problem. In this paper, we include contextual and semantic information in the decoding process of the ECOC framework, defining an ECOC-rank methodology. Altering the ECOC output values by means of the adjacency of classes based on features and class ...

We compare experimentally the performance of three approaches to ensemble-based classi cation on general multi-class datasets. These are the methods of random forest, error-correcting output codes (ECOC) and ECOC enhanced by the use of bootstrapping and classseparability weighting (ECOC-BW). These experiments suggest that ECOCBW yields better generalisation performance than either random forest...

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...

In this paper, we propose a Compact design of Error Correcting Output Codes (ECOC) in terms of the number of dichotomizers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best Compact ECOC code configuration. The results over several challenging multi-class Computer Vision problems show comparable and even better results than stateof-the-art EC...