Cost-Sensitive Learning

Fernández, Alberto; García, Salvador; Galar, Mikel; Prati, Ronaldo C.; Krawczyk, Bartosz; Herrera, Francisco

doi:10.1007/978-3-319-98074-4_4

Cited by 47 publications

(15 citation statements)

References 55 publications

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“…The experiments proved that the ASPCA and CCR modules effectively improved the results of the change detection. In addition, to solve the problem of imbalanced change detection samples, we used the idea of cost-sensitive learning [34][35][36] to assign an adaptive weight for changed samples and unchanged sample loss. Specifically, a mathematical formula for the effective number of samples in [37] was adopted, which preferred to assign higher weights to changed pixels' loss.…”

Section: Attention Mechanismmentioning

confidence: 99%

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Zhang

2021

IJGI

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Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods only utilize fused high-level features from the feature encoder and thus miss the detailed representations that low-level feature pairs contain. Here we propose a multi-level change contextual refinement network (MCCRNet) to strengthen the multi-level change representations of feature pairs. To effectively capture the dependencies of feature pairs while avoiding fusing them, our atrous spatial pyramid cross attention (ASPCA) module introduces a crossed spatial attention module and a crossed channel attention module to emphasize the position importance and channel importance of each feature while simultaneously keeping the scale of input and output the same. This module can be plugged into any feature extraction layer of a Siamese change detection network. Furthermore, we propose a change contextual representations (CCR) module from the perspective of the relationship between the change pixels and the contextual representation, named change region contextual representations. The CCR module aims to correct changed pixels mistakenly predicted as unchanged by a class attention mechanism. Finally, we introduce an effective sample number adaptively weighted loss to solve the class-imbalanced problem of change detection datasets. On the whole, compared with other attention modules that only use fused features from the highest feature pairs, our method can capture the multi-level spatial, channel, and class context of change discrimination information. The experiments are performed with four public change detection datasets of various image resolutions. Compared to state-of-the-art methods, our MCCRNet achieved superior performance on all datasets (i.e., LEVIR, Season-Varying Change Detection Dataset, Google Data GZ, and DSIFN) with improvements of 0.47%, 0.11%, 2.62%, and 3.99%, respectively.

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Section: Attention Mechanismmentioning

confidence: 99%

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Zhang

2021

IJGI

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“…In our model architecture (Table 1), all filters were 3x3 and all convolutional layers had the same underestimate large head motions. To address this issue, we adopted a cost sensitive learning approach [25]. We customized the model's loss function to incorporate a weight proportional to the magnitude of the head move at each data point.…”

Section: Cnn-based Head Pose Estimationmentioning

confidence: 99%

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

Pardoe¹,

Martin²,

Zhao

et al. 2021

Preprint

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Introduction In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are currently no widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. Methods 21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R²). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. Results Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. Conclusions We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

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“…It is necessary to alter the original classifier's algorithm to implement this strategy so that the algorithm considers the different misclassification costs. [31] present a recent review of cost-sensitive approaches.…”

Section: Cost-sensitive Learningmentioning

confidence: 99%

DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets

Carvalho

Prati

2020

Information

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One of the significant challenges in machine learning is the classification of imbalanced data. In many situations, standard classifiers cannot learn how to distinguish minority class examples from the others. Since many real problems are unbalanced, this problem has become very relevant and deeply studied today. This paper presents a new preprocessing method based on Delaunay tessellation and the preprocessing algorithm SMOTE (Synthetic Minority Over-sampling Technique), which we call DTO-SMOTE (Delaunay Tessellation Oversampling SMOTE). DTO-SMOTE constructs a mesh of simplices (in this paper, we use tetrahedrons) for creating synthetic examples. We compare results with five preprocessing algorithms (GEOMETRIC-SMOTE, SVM-SMOTE, SMOTE-BORDERLINE-1, SMOTE-BORDERLINE-2, and SMOTE), eight classification algorithms, and 61 binary-class data sets. For some classifiers, DTO-SMOTE has higher performance than others in terms of Area Under the ROC curve (AUC), Geometric Mean (GEO), and Generalized Index of Balanced Accuracy (IBA).

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Cost-Sensitive Learning

Cited by 47 publications

References 55 publications

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets

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