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Title: Improved performance of error correcting output codes for multiclass classification
Authors: Borg, Jeremy
Keywords: Error-correcting codes (Information theory)
Computer algorithms
Issue Date: 2018
Citation: Borg, J. (2018). Improved performance of error correcting output codes for multiclass classification (Master's dissertation).
Abstract: Some of the best performing classification algorithms to date are only capable of dealing with binary problems, or else require complex adaptations to make them suitable for multi-class situations. A common approach to bypass this issue is to restructure the multi-class problem into several binary ones, then use ensemble techniques to solve each of the binary problems and formulate a single classification result. Some of the most widely known ensemble techniques are the one-vs-one and the one-vs-all. Over recent years, Error-Correcting Output Codes (ECOC) have been devised to improve on this concept by adding a layer of error-correction techniques to the process. Although this concept is still relatively new, research publications show that it has a lot of potential. This dissertation sets out to identify the current state-of-the-art ECOC algorithm with the intention of improving on it. The loss-weighted algorithm published by Escalera et al matched the necessary criteria and was selected as a benchmark algorithm for this project. Whilst implementing it to replicate the results published by the authors, a limitation was noted in the way the algorithm disregards information generated by dichotomies whose input is assumed to belong to a class that was not used during training. Based on this, a new algorithm has been proposed, implemented and tested to establish if classification performance would improve if the data ignored by the loss-weighted algorithm had to be considered. Results show that in general, the proposed ECOC decoding algorithm outperforms the current state-of-the-art counterpart by an average of 2.1%, given that the classification problem meets two specific criteria.
Appears in Collections:Dissertations - FacICT - 2018
Dissertations - FacICTAI - 2018

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