Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

González, Pablo; Alvarez, Eva Rodriguez; Barranquero, José; Díez, Jorge; González-Quirós, R.; Nogueira, E.; López‐Urrutia, Ángel; Coz, Juan José del

doi:10.1109/tnnls.2013.2271535

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…This method is cheaper because physician reviews are not required. Plankton abundance estimation [7,8] is another paradigmatic quantification task, whose goal is to estimate the abundance of some taxonomic groups given plankton samples taken from the oceans.…”

Section: Introductionmentioning

confidence: 99%

Dynamic ensemble selection for quantification tasks

et al. 2019

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Ensembles are among the most effective and successful methods for almost all supervised tasks. Not long ago, an ensemble approach has been proposed for quantification learning. The idea of such method is to exploit the prior knowledge about quantification tasks, building ensembles in which diversity is achieved by training each model with a different distribution. These training samples are generated taking into account the expected drift in class distribution. This paper extends this method proposing three new quantifier selection criteria particularly devised for quantification problems, where two of them are defined for dynamic ensemble selection. The experiments demonstrate that, in many cases, these selection functions outperform straightforward approaches, like averaging all models and using quantification accuracy to prune the ensemble. Moreover, the results show that performance heavily depends on the combination of the base quantification algorithm and the selection measure.

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Section: Introductionmentioning

confidence: 99%

Dynamic ensemble selection for quantification tasks

et al. 2019

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“…We believe that the structural information also exists in other learning tasks (e.g., one-class learning [15], [33], ordinalclass learning [31], [34], multiclass learning [35], [36], and so on). In the future, we plan to extend SMPM to one-class learning [15], [33], ordinal-class learning [31], [34], multiclass learning [35], [36], and other learning tasks. Although the binary search procedure can solve SMPM effectively, the running time is not fast enough especially when the training data size (or the feature size) is large.…”

Section: Discussionmentioning

confidence: 99%

Structural Minimax Probability Machine

Sun

Sheng

2017

IEEE Trans. Neural Netw. Learning Syst.

262

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Minimax probability machine (MPM) is an interesting discriminative classifier based on generative prior knowledge. It can directly estimate the probabilistic accuracy bound by minimizing the maximum probability of misclassification. The structural information of data is an effective way to represent prior knowledge, and has been found to be vital for designing classifiers in real-world problems. However, MPM only considers the prior probability distribution of each class with a given mean and covariance matrix, which does not efficiently exploit the structural information of data. In this paper, we use two finite mixture models to capture the structural information of the data from binary classification. For each subdistribution in a finite mixture model, only its mean and covariance matrix are assumed to be known. Based on the finite mixture models, we propose a structural MPM (SMPM). SMPM can be solved effectively by a sequence of the second-order cone programming problems. Moreover, we extend a linear model of SMPM to a nonlinear model by exploiting kernelization techniques. We also show that the SMPM can be interpreted as a large margin classifier and can be transformed to support vector machine and maxi-min margin machine under certain special conditions. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of SMPM.

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“…-Meteorología [Tsai et al, 2009]; -Biología [González et al, 2013]; -y servicios de información en Internet, como buscadores y redes sociales [Rao and Pais, 2019] [Basak et al, 2019] [Fard et al, 2019; por citar algunos ejemplos representativos. En definitiva, todo problema de detección de anomalías (o sucesos poco probables) es susceptible de verse afectado por los efectos del desequilibrio en las tareas de clasificación por medio de máquinas de aprendizaje.…”

Section: Capítulo 2 Problemas De Clasificación Desequilibradaunclassified

On the design of Bayesian principled algorithms for imbalanced classification

Benítez-Buenache

Álvarez-Pérez

Figueiras-Vidal

2021

Knowledge-Based Systems

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Singular Problems are those whose characteristics compromise the correct operation of conventional discriminative machines, obtaining unsatisfactory results. Among them, imbalanced classification problems stand out, those in which there are large differences in the class populations or/and the cost policy penalizes to a greater extent the choice of certain classes, biasing the machine output in favor of the predominant classes. Therefore, the application of specific methods that compensate the imbalance is required, allowing the detection of the minority classes.Particularly for the binary case, a state-of-the-art survey of the existing rebalancing methods is carried out. Most of the proposed techniques are purely empirical, without a complete analysis of the statistical implications of their application.Although their use may provide good results under certain conditions, any change in these conditions may lead to a degradation of their performance. Therefore, a principled methodology based on Bayesian statistical theory is presented with the aim of constructing robust solutions. This methodology is based on the likelihood ratio invariance principle, for which two sufficient and necessary conditions are established: the use of Bregman divergences as a surrogate cost and statistically neutral rebalancing methods. In addition, principled two-step classification procedures are proposed and a rebalanced design process based on the combination of methods is described in detail. Several experiments support the methodology, studying its effects and limitations in real problems under different circumstances: larger or smaller number of available samples and presence of noise.Finally, the SMOTE algorithm, one of the most common rebalancing methods, is studied in more depth. Due to the filiform generation of samples −by means of the nearest neighbors−, SMOTE presents difficulties with high dimensionality problems.Therefore, an alternative, VoluSMOTE, is proposed to correct or mitigate such effects by volumetric generation."La perfeccción es una pulida corrección de errores"

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Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

Cited by 19 publications

References 18 publications

Dynamic ensemble selection for quantification tasks

Dynamic ensemble selection for quantification tasks

Structural Minimax Probability Machine

On the design of Bayesian principled algorithms for imbalanced classification

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