Fuzzy Data Augmentation for Handling Overlapped and Imbalanced Data

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

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A fuzzy data augmentation technique to improve regularisation

International Journal of Intelligent Systems ◽

10.1002/int.22731 ◽

2021 ◽

Author(s):

Rukshima Dabare ◽

Kok Wai Wong ◽

Mohd Fairuz Shiratuddin ◽

Polychronis Koutsakis

Keyword(s):

Data Augmentation ◽

Fuzzy Data

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Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

Sensors ◽

10.3390/s18113744 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3744 ◽

Cited By ~ 4

Author(s):

Jaehun Bang ◽

Taeho Hur ◽

Dohyeong Kim ◽

Thien Huynh-The ◽

Jongwon Lee ◽

...

Keyword(s):

Emotion Recognition ◽

Data Augmentation ◽

Imbalanced Data ◽

Sampling Technique ◽

Cold Start ◽

Training Model ◽

Small Sample ◽

Speech Emotion Recognition ◽

Target User ◽

Cold Start Problem

Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.

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Generative inter-class transformations for imbalanced data weather classification

London Imaging Meeting ◽

10.2352/issn.2694-118x.2021.lim-16 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 16-20

Author(s):

Apostolia Tsirikoglou ◽

Marcus Gladh ◽

Daniel Sahlin ◽

Gabriel Eilertsen ◽

Jonas Unger

Keyword(s):

Data Augmentation ◽

Imbalanced Data ◽

Training Data ◽

Generative Adversarial Networks ◽

Medical Applications ◽

Single Image ◽

Adversarial Networks ◽

Augmentation Strategies ◽

Future Work ◽

Small Dataset

This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training data in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there is a limit to how much improvements can be gained using classical augmentation strategies. Generative adversarial networks (GAN) have been demonstrated to generate impressive results, and have also been successful as a tool for data augmentation, but mostly for images of limited diversity, such as in medical applications. We investigate the possibilities in using generative augmentations for balancing a small weather classification dataset, where one class has a reduced number of images. We compare intra-class augmentations by means of classical transformations as well as noise-to-image GANs, to interclass augmentations where images from another class are transformed to the underrepresented class. The results show that it is possible to take advantage of GANs for inter-class augmentations to balance a small dataset for weather classification. This opens up for future work on GAN-based augmentations in scenarios where data is both diverse and scarce.

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Semantic Segmentation of High-Resolution Airborne Images With Dual-Stream DeepLabV3+

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010023 ◽

2021 ◽

Vol 11 (1) ◽

pp. 23

Author(s):

Ozgun Akcay ◽

Ahmet Cumhur Kinaci ◽

Emin Ozgur Avsar ◽

Umut Aydar

Keyword(s):

Data Augmentation ◽

Contextual Information ◽

Imbalanced Data ◽

Semantic Segmentation ◽

Multi Scale ◽

Segmentation Algorithms ◽

Geographic Datasets ◽

Stream Architecture ◽

The Given

In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown.

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Impact of Balancing Techniques for Imbalanced Class Distribution on Twitter Data for Emotion Analysis

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-7371-6.ch012 ◽

2021 ◽

pp. 211-231

Author(s):

Shivani Vasantbhai Vora ◽

Rupa G. Mehta ◽

Shreyas Kishorkumar Patel

Keyword(s):

Data Augmentation ◽

Imbalanced Data ◽

Training Data ◽

Human Needs ◽

Class Distribution ◽

Redundant Data ◽

Imbalanced Class ◽

Understanding Emotions ◽

Imbalanced Class Distribution

Continuously growing technology enhances creativity and simplifies humans' lives and offers the possibility to anticipate and satisfy their unmet needs. Understanding emotions is a crucial part of human behavior. Machines must deeply understand emotions to be able to predict human needs. Most tweets have sentiments of the user. It inherits the imbalanced class distribution. Most machine learning (ML) algorithms are likely to get biased towards the majority classes. The imbalanced distribution of classes gained extensive attention as it has produced many research challenges. It demands efficient approaches to handle the imbalanced data set. Strategies used for balancing the distribution of classes in the case study are handling redundant data, resampling training data, and data augmentation. Six methods related to these techniques have been examined in a case study. Upon conducting experiments on the Twitter dataset, it is seen that merging minority classes and shuffle sentence methods outperform other techniques.

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Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data

Remote Sensing ◽

10.3390/rs12203431 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3431 ◽

Cited By ~ 1

Author(s):

Mariano Cabezas ◽

Sarah Kentsch ◽

Luca Tomhave ◽

Jens Gross ◽

Maximo Larry Lopez Caceres ◽

...

Keyword(s):

Data Augmentation ◽

Imbalanced Data ◽

Practical Interest ◽

True Positive Rate ◽

Ease Of Use ◽

Aerial Images ◽

Specific Class ◽

Final Layer ◽

Random Weights ◽

Positive Rate

Deep Learning (DL) has become popular due to its ease of use and accuracy, with Transfer Learning (TL) effectively reducing the number of images needed to solve environmental problems. However, this approach has some limitations which we set out to explore: Our goal is to detect the presence of an invasive blueberry species in aerial images of wetlands. This is a key problem in ecosystem protection which is also challenging in terms of DL due to the severe imbalance present in the data. Results for the ResNet50 network show a high classification accuracy while largely ignoring the blueberry class, rendering these results of limited practical interest to detect that specific class. Moreover, by using loss function weighting and data augmentation results more akin to our practical application, our goals can be obtained. Our experiments regarding TL show that ImageNet weights do not produce satisfactory results when only the final layer of the network is trained. Furthermore, only minor gains are obtained compared with random weights when the whole network is retrained. Finally, in a study of state-of-the-art DL architectures best results were obtained by the ResNeXt architecture with 93.75 True Positive Rate and 98.11 accuracy for the Blueberry class with ResNet50, Densenet, and wideResNet obtaining close results.

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Large-Scale Coarse-to-Fine Object Retrieval Ontology and Deep Local Multitask Learning

Computational Intelligence and Neuroscience ◽

10.1155/2019/1483294 ◽

2019 ◽

Vol 2019 ◽

pp. 1-40

Author(s):

Ngoc Q. Ly ◽

Tuong K. Do ◽

Binh X. Nguyen

Keyword(s):

Network Architecture ◽

Large Scale ◽

Data Augmentation ◽

Imbalanced Data ◽

Recall Rate ◽

Coarse Grained ◽

Object Retrieval ◽

Fine Grained ◽

Object Ontology ◽

Coarse To Fine

Object retrieval plays an increasingly important role in video surveillance, digital marketing, e-commerce, etc. It is facing challenges such as large-scale datasets, imbalanced data, viewpoint, cluster background, and fine-grained details (attributes). This paper has proposed a model to integrate object ontology, a local multitask deep neural network (local MDNN), and an imbalanced data solver to take advantages and overcome the shortcomings of deep learning network models to improve the performance of the large-scale object retrieval system from the coarse-grained level (categories) to the fine-grained level (attributes). Our proposed coarse-to-fine object retrieval (CFOR) system can be robust and resistant to the challenges listed above. To the best of our knowledge, the new main point of our CFOR system is the power of mutual support of object ontology, a local MDNN, and an imbalanced data solver in a unified system. Object ontology supports the exploitation of the inner-group correlations to improve the system performance in category classification, attribute classification, and conducting training flow and retrieval flow to save computational costs in the training stage and retrieval stage on large-scale datasets, respectively. A local MDNN supports linking object ontology to the raw data, and an imbalanced data solver based on Matthews’ correlation coefficient (MCC) addresses that the imbalance of data has contributed effectively to increasing the quality of object ontology realization without adjusting network architecture and data augmentation. In order to evaluate the performance of the CFOR system, we experimented on the DeepFashion dataset. This paper has shown that our local MDNN framework based on the pretrained NASNet architecture has achieved better performance (14.2% higher in recall rate) compared to single-task learning (STL) in the attribute learning task; it has also shown that our model with an imbalanced data solver has achieved better performance (5.14% higher in recall rate for fewer data attributes) compared to models that do not take this into account. Moreover, MAP@30 hovers 0.815 in retrieval on an average of 35 imbalanced fashion attributes.

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AUGMENTING DATA USING GAUSSIAN MIXTURE EMBEDDING FOR IMPROVING LAND COVER SEGMENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-w2-2020-71-2020 ◽

2020 ◽

Vol IV-3/W2-2020 ◽

pp. 71-76

Author(s):

D. A. B. Oliveira

Keyword(s):

Land Cover ◽

Mixture Models ◽

Data Augmentation ◽

Imbalanced Data ◽

Semantic Segmentation ◽

Gaussian Mixture ◽

Data Representation ◽

Training Time ◽

Training Samples ◽

Displacement Maps

Abstract. The use of convolutional neural networks improved greatly data synthesis in the last years and have been widely used for data augmentation in scenarios where very imbalanced data is observed, such as land cover segmentation. Balancing the proportion of classes for training segmentation models can be very challenging considering that samples where all classes are reasonably represented might constitute a small portion of a training set, and techniques for augmenting this small amount of data such as rotation, scaling and translation might be not sufficient for efficient training. In this context, this paper proposes a methodology to perform data augmentation from few samples to improve the performance of CNN-based land cover semantic segmentation. First, we estimate the latent data representation of selected training samples by means of a mixture of Gaussians, using an encoder-decoder CNN. Then, we change the latent embedding used to generate the mixture parameters, at random and in training time, to generate new mixture models slightly different from the original. Finally, we compute the displacement maps between the original and the modified mixture models, and use them to elastically deform the original images, creating new realistic samples out of the original ones. Our disentangled approach allows the spatial modification of displacement maps to preserve objects where deformation is undesired, like buildings and cars, where geometry is highly discriminant. With this simple pipeline, we managed to augment samples in training time, and improve the overall performance of two basal semantic segmentation CNN architectures for land cover semantic segmentation.

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