Error correcting output codes (ECOC) have been proposed to enhance generalization ability of classifiers. If, instead of discrete error functions, continuous error functions are used, unclassifiable regions of multiclass support vector machines are resolved. In this paper, we discuss minimum operations as well as average operations for error functions of support vector machines and show the equ...

Error-correcting output codes (ECOC) are used to design diverse classifier ensembles. Diversity within ECOC is traditionally measured by Hamming distance. Here we argue that this measure is insufficient for assessing the quality of code for the purposes of building accurate ensembles. We propose to use diversity measures from the literature on classifier ensembles and suggest an evolutionary al...

In previous work, it has been experimentally shown that the implementation of Error Correcting Output Coding (ECOC) classification methods with an ensemble of parallel and independent non linear dichotomizers (ECOC PND) outperforms the implementation with a single monolithic multi layer perceptron (ECOC MLP). This result was ascribed to the higher effectiveness of error correcting output coding...

In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-den...

The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same...

In this work, we propose a novel Genetic Inspired Error Correcting Output Codes (ECOC) Optimization, which looks for an efficient problem-dependent encoding of the multi-class task with high generalization performance. This optimization procedure is based on novel ECOCCompliant crossover, mutation, and extension operators, which guide the optimization process to promising regions of the search ...

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...

We investigate the use of Error-Correcting Output Codes (ECOC) for efficient text classification with a large number of categories and propose several extensions which improve the performance of ECOC. ECOC has been shown to perform well for classification tasks, including text classification, but it still remains an under-explored area in ensemble learning algorithms. We explore the use of erro...

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...