A scalable pairwise class interaction framework for multidimensional classification

Arias, Jacinto; Gámez, José A.; Nielsen, Thomas D.; Puerta, José M.

doi:10.1016/j.ijar.2015.07.007

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…For each figure, each row corresponds to the binary classification model of one class label. The first third of the columns correspond to the discrete version of kNN-augmented features in (10), the middle third of the columns to the continuous version of kNN-augmented features in (11), and the last third of the columns to the maximum margin-augmented features in (12). It is shown that, for each third of all columns, the diagonal element usually takes the largest value in its corresponding row.…”

Section: Further Analysis 1) Effectiveness Of Algorithmic Designmentioning

confidence: 99%

“…Under the MDC setting, each object is represented by a single instance while associated with multiple class variables, each corresponding to a specific class space characterizing the object′s semantics along one specific dimension. Specifically, the MDC problem widely exists in many application scenarios, such as bioinformatics [4,5] , text classification [6,7] , computer vision [8−10] , re-source allocation [11] , etc. Fig.…”

Section: Introductionmentioning

confidence: 99%

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Multi-dimensional Classification via Selective Feature Augmentation

Jia

Zhang

2022

Mach. Intell. Res.

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In multi-dimensional classification (MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features. In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features. Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension’s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard kNN, weighted kNN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

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Section: Further Analysis 1) Effectiveness Of Algorithmic Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-dimensional Classification via Selective Feature Augmentation

Jia

Zhang

2022

Mach. Intell. Res.

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“…The wrapper strategy of Bielza et al (2011) is used for learning general DAG-DAG MBCs. Arias et al (2016) presented a meta-classifier for multi-dimensional classification, although its relationship with MBCs is frail and we do not include it in Table 3. In the first stage, a base classifier is learned for each pair of class variables (encoding their joint distribution, in contrast to base classifiers of the multi-label pairwise methods proposed by Hüllermeier et al (2008) and Fürnkranz et al (2008) which encode the preference between them).…”

Section: Meta-classifiersmentioning

confidence: 99%

Multi-dimensional Bayesian network classifiers: A survey

2020

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Multi-dimensional classification is a cutting-edge problem, in which the values of multiple class variables have to be simultaneously assigned to a given example. It is an extension of the well known multi-label subproblem, in which the class variables are all binary. In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers. We focus on multi-dimensional Bayesian network classifiers, which directly cope with multi-dimensional classification and consider dependencies among class variables. A comprehensive survey of this state-of-the-art classification model is offered by covering aspects related to their learning and inference process complexities. We also describe algorithms for structural learning, provide real-world applications where they have been used, and compile a collection of related software.

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“…Hence, it is intractable when the number of class variables is not small. [13] proposes a method that learns an undirected graph between the class variables, and learns a base model (e.g., naïve Bayes) for each pair of connected class variables. The base model gives a factor over a pair of class variables given an instance of the feature variables.…”

Section: Previous Work On Learning Mbcsmentioning

confidence: 99%

Tractability of most probable explanations in multidimensional Bayesian network classifiers

Benjumeda

Bielza

Larrañaga

2018

International Journal of Approximate Reasoning

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Multidimensional Bayesian network classifiers have gained popularity over the last few years due to their expressive power and their intuitive graphical representation. A drawback of this approach is that their use to perform multidimensional classification, a generalization of multi-label classification, can be very computationally demanding when there are a large number of class variables. Thus, a key challenge in this field is to ensure the tractability of these models during the learning process. In this paper, we show how information about the most common queries of multidimen-sional Bayesian network classifiers affects the complexity of these models. We provide upper bounds for the complexity of the most probable explanations and marginals of class variables conditioned to an instantiation of all feature variables. We use these bounds to propose efficient strategies for bounding the complexity of multidimensional Bayesian network classifiers during the learning process, and provide a simple learning method with an order-based search that guarantees the tractability of the returned models. Experimental results show that our approach is competitive with other methods in the state of the art and also ensures the tractability of the learned models.

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A scalable pairwise class interaction framework for multidimensional classification

Cited by 15 publications

References 20 publications

Multi-dimensional Classification via Selective Feature Augmentation

Multi-dimensional Classification via Selective Feature Augmentation

Multi-dimensional Bayesian network classifiers: A survey

Tractability of most probable explanations in multidimensional Bayesian network classifiers

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