A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains

Gonçalves, Eduardo Corrêa; Plastino, Alexandre; Freitas, Alex A.

doi:10.1109/ictai.2013.76

Cited by 67 publications

(53 citation statements)

References 22 publications

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“…We conduct experiments on Land Cover and five other publicly-available multi-label image datasets, Flags [53], Scene [13], Corel5k [54], MIRFlickr [55] and ESPGame [56], to validate the performance of our proposed MLC-LRR and compare it with five representative and related graph-based multi-label classifiers: MSC [20], TMC [27], FCML [29], Tram [19] and DLP [31]. MSC is a supervised multi-label classifier, and the other four are semi-supervised multi-label classifiers.…”

Section: Resultsmentioning

confidence: 99%

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

et al. 2017

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Abstract:Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.

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Section: Resultsmentioning

confidence: 99%

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

et al. 2017

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“…Since the label ordering in CC is composed at random [4,14,15], we design ensembles classifier chains under double layer to train L DCC classifiers 1 ,..., L h h . Each classifier is unique and able to give different multi-label predictions.…”

Section: Ensembles Of DCCmentioning

confidence: 99%

“…Eduardo [15] demonstrated that the label ordering of CC has a strong effect on predictive accuracy. To deal with this problem, [15] proposed a genetic algorithm for optimizing the label ordering in classifier chains. Since some of the parameters in genetic algorithm are sensitive, the manually parameters setting may reduce the predictive performance.…”

Section: Introductionmentioning

confidence: 99%

“…Since some of the parameters in genetic algorithm are sensitive, the manually parameters setting may reduce the predictive performance. Also, in CC, the first binary classifiers will possibly generate wrong predictions and course significant error propagation along the chain [14,15]. To solve this problem, the authors of CC propose combining random orders through an ensemble of classifier chains (ECC) in [4,14].…”

Section: Introductionmentioning

confidence: 99%

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Double Layer Based Multi-label Classifier Chain

Guo¹,

Li²

2015

TOCSJ

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Abstract:In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for each unseen instance. The widely known binary relevance method (BR) for multi-label classification considers each label as an independent binary problem. It is ignored in the literature due to inadequacy of not considering label correlations. In this paper, we present our double layer based classifier chains method (DCC), which overcomes disadvantages of BR and inherits the benefit of classifier chain method (CC). This algorithm decomposes the multi-label classification problem into two classification processes to generate classifier chain. Each classifier in the chain is responsible for learning and predicting the binary association of the label given the attribute space expanded by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. We also extend this approach further in an ensemble framework. An extensive evaluation covers a broad range of multi-label datasets with a variety of evaluation measures specifically designed for multi-label classification. Experiments on benchmark datasets validate the effectiveness of proposed approach comparing with state-of-art methods in terms of average ranking.

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“…Examples of such algorithms are the adaptation of the decision tree learning algorithm for MLC (Vens et al 2008), support-vector machines for MLC (Gonçalves et al 2013), k-nearest neighbours for MLC (Zhang and Zhou 2005), instance based learning for MLC (Cheng and Hüllermeier 2009), and others.…”

Section: Multi-label Classificationmentioning

confidence: 99%

Multi-label classification via multi-target regression on data streams

2016

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Multi-label classification (MLC) tasks are encountered more and more frequently in machine learning applications. While MLC methods exist for the classical batch setting, only a few methods are available for streaming setting. In this paper, we propose a new methodology for MLC via multi-target regression in a streaming setting. Moreover, we develop a streaming multi-target regressor iSOUP-Tree that uses this approach. We experimentally compare two variants of the iSOUP-Tree method (building regression and model trees), as well as ensembles of iSOUP-Trees with state-of-the-art tree and ensemble methods for MLC on data streams. We evaluate these methods on a variety of measures of predictive performance (appropriate for the MLC task). The ensembles of iSOUP-Trees perform significantly better on some of these measures, especially the ones based on label ranking, and are not significantly worse than the competitors on any of the remaining measures. We identify the thresholding problem for the task of MLC on data streams as a key issue that needs to be addressed in order to obtain even better results in terms of predictive performance.

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A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains

Cited by 67 publications

References 22 publications

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

Double Layer Based Multi-label Classifier Chain

Multi-label classification via multi-target regression on data streams

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