Classification of imbalanced land-use/land-cover data using variational semi-supervised learning

Cenggoro, Tjeng Wawan; Isa, Sani Muhamad; Kusuma, Gede Putra; Pardamean, Bens

doi:10.1109/innocit.2017.8319149

Cited by 29 publications

(10 citation statements)

References 9 publications

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“…According to our experiment, performance can be further increased by using G-SMOTE. A number of other studies [21,23] did not use specific imbalanced metrics; therefore, they cannot be directly compared to our results.…”

Section: Discussionmentioning

confidence: 95%

“…SMOTE is the most popular informed oversampling method, and it has been used to successfully deal with the class imbalance problem in land cover classification [23]. In this approach, the minority class is oversampled by randomly selecting a minority class instance and generating synthetic examples along the line segment joining it with one of its minority class neighbors.…”

Section: Informed Resamplingmentioning

confidence: 99%

“…A number of studies report significant improvements in LULC mapping accuracy with the use of SMOTE oversampling. For instance, the variational semi-supervised learning (VSSL) proposed by [23] aims to deal with the imbalance problem in LULC mapping. VSSL is a semi-supervised learning framework consisting of a deep generative model.…”

Section: Informed Resamplingmentioning

confidence: 99%

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Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

et al. 2019

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The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce accurate maps can have a significant impact on the way we manage and optimize natural resources. The difficulty in achieving these results comes from many different factors, such as data quality and uncertainty. In this paper, we address the imbalanced learning problem, a common and difficult conundrum in remote sensing that affects the quality of classification results, by proposing Geometric-SMOTE, a novel oversampling method, as a tool for addressing the imbalanced learning problem in remote sensing. Geometric-SMOTE is a sophisticated oversampling algorithm which increases the quality of the instances generated in previous methods, such as the synthetic minority oversampling technique. The performance of Geometric- SMOTE, in the LUCAS (Land Use/Cover Area Frame Survey) dataset, is compared to other oversamplers using a variety of classifiers. The results show that Geometric-SMOTE significantly outperforms all the other oversamplers and improves the robustness of the classifiers. These results indicate that, when using imbalanced datasets, remote sensing researchers should consider the use of these new generation oversamplers to increase the quality of the classification results.

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Section: Discussionmentioning

confidence: 95%

Section: Informed Resamplingmentioning

confidence: 99%

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Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

et al. 2019

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“…This condition generally leads to poor performance for the labels with fewer images. This problem is called imbalanced data problem and is known to cause diminishing performance for machine learning models as well as deep learning models [39][40][41]. In counting case, one of the possible solutions to this problem is to create a model that is capable to extrapolate its count prediction to count labels with fewer data.…”

Section: Possible Challengesmentioning

confidence: 99%

Using transfer learning for smart building management system

et al. 2019

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In building management, energy optimization is one of the main concern that needs to be automated. For automation, an intelligent system needs to be developed. However, an intelligent system needs to be trained in a large dataset before it can be used reliably. In this paper, we present a transfer learning scheme to develop an intelligent system for smart building management system. Specifically, the intelligent system is able to count human inside a room, which can be utilized to adaptively adjust energy usage in a room. The transfer learning scheme employs a deep learning model that is pretrained on ImageNet dataset. To enable the human counting capability, the model is trained on a dataset specifically collected for human counting case.

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“…Imbalance data has been proved can decrease the performance of machine learning algorithm 1 , where imbalance data means the total of data from each class is significantly different. The example of imbalance data can be seen in the works from Cenggoro et al 2,3 . The data from those researches shows that classes of an urban area is up to 62.45% from the total area, while the class of an open area is only 0.02% from the total area.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network

Fanny

Cenggoro

2018

Procedia Computer Science

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Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification.

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Classification of imbalanced land-use/land-cover data using variational semi-supervised learning

Cited by 29 publications

References 9 publications

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

Using transfer learning for smart building management system

Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network

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