7,761 research outputs found

    An ensemble approach of dual base learners for multi-class classification problems

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    In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient. (C) 2014 Elsevier B.V. All rights reserved.This research was supported by the Spanish MICINN under Projects TRA2010-20225-C03-01, TRA 2011-29454-C03-02, and TRA 2011-29454-C03-03

    RandomBoost: Simplified Multi-class Boosting through Randomization

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    We propose a novel boosting approach to multi-class classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The approach uses random projections to alleviate the proliferation of binary classifiers typically required to perform multi-class classification. The result is a multi-class classifier with a single vector-valued parameter, irrespective of the number of classes involved. Two variants of this approach are proposed. The first method randomly projects the original data into new spaces, while the second method randomly projects the outputs of learned weak classifiers. These methods are not only conceptually simple but also effective and easy to implement. A series of experiments on synthetic, machine learning and visual recognition data sets demonstrate that our proposed methods compare favorably to existing multi-class boosting algorithms in terms of both the convergence rate and classification accuracy.Comment: 15 page

    Totally Corrective Multiclass Boosting with Binary Weak Learners

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    In this work, we propose a new optimization framework for multiclass boosting learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two successful multiclass boosting algorithms, which can use binary weak learners. We explicitly derive these two algorithms' Lagrange dual problems based on their regularized loss functions. We show that the Lagrange dual formulations enable us to design totally-corrective multiclass algorithms by using the primal-dual optimization technique. Experiments on benchmark data sets suggest that our multiclass boosting can achieve a comparable generalization capability with state-of-the-art, but the convergence speed is much faster than stage-wise gradient descent boosting. In other words, the new totally corrective algorithms can maximize the margin more aggressively.Comment: 11 page

    Coupling different methods for overcoming the class imbalance problem

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    Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357
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