Multi-label Collective Classification Using Adaptive Neighborhoods

Saha, Tanwistha; Rangwala, Huzefa; Domeniconi, Carlotta

doi:10.1109/icmla.2012.77

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…This process continues until the algorithm converges. Saha et al (2012) have recently described an iterative convergence algorithm to deal with multi-label classification problems. Finally, collective classification has been recently investigated in combination semi-supervised and transductive learning (Shi et al 2011;McDowell and Aha 2012).…”

Section: Collective Inferencementioning

confidence: 99%

Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system

2016

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Remotely sensed hyperspectral image classification is a very challenging task due to the spatial correlation of the spectral signature and the high cost of true sample labeling. In light of this, the collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. In this paper, both these paradigms contribute to the definition of a spectral-relational classification methodology for imagery data. We propose a novel algorithm to assign a class to each pixel of a sparsely labeled hyperspectral image. It integrates the spectral information and the spatial correlation through an ensemble system. For every pixel of a hyperspectral image, spatial neighborhoods are constructed and used to build application-specific relational features. Classification is performed with an ensemble comprising a classifier learned by considering the available spectral information (associated with the pixel) and the classifiers learned by considering the extracted spatio-relational information (associated with the spatial neighborhoods). The more reliable labels predicted by the ensemble are fed back to the labeled part of the image. Experimental results highlight the importance of the spectral-relational strategy for the accurate transductive classification of hyperspectral images and they validate the proposed algorithm.

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Section: Collective Inferencementioning

confidence: 99%

Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system

2016

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“…This process continues until the algorithm converges. Saha et al (2012) have recently described an iterative convergence algorithm to deal with multi-label classification problems. McDowell and Aha (2013) have shown that the accuracy of collective classification performed with both iterative convergence approaches and Gibbs sampling approaches may be increased by including, for each node, the descriptive attributes of the neighboring nodes as relational attributes.…”

Section: Related Workmentioning

confidence: 99%

Collective regression for handling autocorrelation of network data in a transductive setting

2015

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Sensor networks, communication and financial networks, web and social networks are becoming increasingly important in our day-to-day life. They contain entities which may interact with one another. These interactions are often characterized by a form of autocorrelation, where the value of an attribute at a given entity depends on the values at the entities it is interacting with. In this situation, the collective inference paradigm offers a unique opportunity to improve the performance of predictive models on network data, as interacting instances are labeled simultaneously by dealing with autocorrelation. Several recent works have shown that collective inference is a powerful paradigm, but it is mainly developed with a fully-labeled training network. In contrast, while it may be cheap to acquire the network topology, it may be costly to acquire node labels for training. In this paper, we examine how to explicitly consider autocorrelation when performing regression inference within network data. In particular, we study the transduction of collective regression when a sparsely labeled network is a common situation. We present an algorithm, called CORENA (COllective REgression in Network dAta), to assign a numeric label to each instance in the network. In particular, we iteratively augment the representation of each instance with instances sharing correlated representations across the network. In this way, the proposed learning model is able to capture autocorrelations of labels over a group of related instances and feed-back the more reliable labels predicted by the transduction in the labeled network. Empirical studies demonstrate that the proposed approach can boost regression performances in several spatial and social tasks

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“…The difference is that it is applied to heterogeneous networks and not to logic clauses. Connections can also be found with the task of multi-label collective classification [23,38], where, however, objects to be classified are of the same type.…”

Section: Introductionmentioning

confidence: 99%