Balancing thermal comfort datasets

Quintana, Matias; Schiavon, Stefano; Tham, Kwok Wai; Miller, Clayton

doi:10.1145/3408308.3427612

Cited by 18 publications

(12 citation statements)

References 22 publications

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“…This study also collected temporal data from fixed IEQ sensors in the indoor environment (temperature, humidity, noise, and carbon dioxide) and physiological data from the smartwatch (heart rate, near body temperature). The data from this study was used to build personal comfort models [28], analyze the comfort behavior for the transition period between spaces [46], and understand the value of class balancing for thermal comfort prediction [47]. This paper builds upon this work by adding the building's spatial data (from the BIM model) and the spatialtemporal data of occupants related to their environment (location of people as compared to the sensors).…”

Section: Methodsmentioning

confidence: 99%

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

Abdelrahman,

Chong,

Miller

2021

Preprint

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Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person's thermal preference. The spatial context of a building can provide information to models about the windows, walls, heating and cooling sources, air diffusers, and other factors that create micro-environments that influence thermal comfort. Due to spatial heterogeneity, it is impractical to position sensors at a high enough resolution to capture all conditions. This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting spatial-temporal occupants' indoor environmental preferences. Build2Vec utilizes the spatial data from the Building Information Model (BIM) and indoor localization in a real-world setting. This framework uses longitudinal intensive thermal comfort subjective feedback from smart watch-based ecological momentary assessments (EMA). The aggregation of these data is combined into a graph network structure (i.e., objects and relations) and used as input for a classification model to predict occupant thermal preference. The results of a test implementation show 14-28% accuracy improvement over a set of baselines that use conventional thermal preference prediction input variables.

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Section: Methodsmentioning

confidence: 99%

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

Abdelrahman,

Chong,

Miller

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…To that end, the same core of researchers in [55] expanded the scope of their study in [64], including more training datasets and hyper-parametrizing a bespoke model, called ComfortGAN. ComfortGAN relies on the CGAN architecture with Wasserstein loss and a gradient penalty.…”

Section: Other Disciplinesmentioning

confidence: 99%

“…Once again, ComfortGAN retained its statistical advantage, but the margin was less pronounced. This led [64] to speculate that for the research problem in question, the complexity and computational load of a GAN-based approach may not be justified, particularly when, as in the case with human comfort survey data, classes can be combined, and greater emphasis can be placed on fine-tuning the machine learning models themselves rather than data augmentation.…”

Section: Other Disciplinesmentioning

confidence: 99%

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Sauber-Cole

Khoshgoftaar

2022

J Big Data

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The existence of class imbalance in a dataset can greatly bias the classifier towards majority classification. This discrepancy can pose a serious problem for deep learning models, which require copious and diverse amounts of data to learn patterns and output classifications. Traditionally, data-level and algorithm-level techniques have been instrumental in mitigating the adverse effect of class imbalance. With the recent development and proliferation of Generative Adversarial Networks (GANs), researchers across a variety of disciplines have adapted the architecture of GANs and implemented them on imbalanced datasets to generate instances of the underrepresented class(es). Though the bulk of research has been centered on the application of this methodology in computer vision tasks, GANs are likewise being appropriated for use in tabular data, or data consisting of rows and columns with traditional structured data types. In this survey paper, we assess the methodology and efficacy of these modifications on tabular datasets, across domains such network traffic classification and financial transactions over the past seven years. We examine what methodologies and experimental factors have resulted in the greatest machine learning efficacy, as well as the research works and frameworks which have proven most influential in the development of the application of GANs in tabular data settings. Specifically, we note the prevalence of the CGAN architecture, the optimality of novel methods with CNN learners and minority-class sensitive measures such as F1 score, the popularity of SMOTE as a baseline technique, and the improved performance in the year-over-year use of GANs in imbalanced tabular datasets.

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“…Generating the minority samples is regarded as an effective way to solve the class imbalance problem, which is classified as oversampling. Random oversampling, synthetic minority oversampling technique (SMOTE) [12], and borderline SMOTE [16] are considered to be the best traditional oversampling algorithms. However, when the data have a high dimensional space, the performance reduces significantly.…”

Section: Gan and Imbalancedmentioning

confidence: 99%

“…But machine learning and deep learning techniques appear in time to make up for the shortcomings of the traditional methods [15]. Generative adversarial network (GAN) is a typical generative model, which is regarded as a potential solution for imbalanced data by generating new samples for the minority class [16,17]. GAN is composed of two neural networks: generator and discriminator.…”

Section: Introductionmentioning

confidence: 99%

Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

Zhang

Qiu

et al. 2021

Security and Communication Networks

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Class imbalance is a common problem in network threat detection. Oversampling the minority class is regarded as a popular countermeasure by generating enough new minority samples. Generative adversarial network (GAN) is a typical generative model that can generate any number of artificial minority samples, which are close to the real data. However, it is difficult to train GAN, and the Nash equilibrium is almost impossible to achieve. Therefore, in order to improve the training stability of GAN for oversampling to detect the network threat, a convergent WGAN-based oversampling model called convergent WGAN (CWGAN) is proposed in this paper. The training process of CWGAN contains multiple iterations. In each iteration, the training epochs of the discriminator are dynamic, which is determined by the convergence of discriminator loss function in the last two iterations. When the discriminator is trained to convergence, the generator will then be trained to generate new minority samples. The experiment results show that CWGAN not only improve the training stability of WGAN on the loss smoother and closer to 0 but also improve the performance of the minority class through oversampling, which means that CWGAN can improve the performance of network threat detection.

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Balancing thermal comfort datasets

Cited by 18 publications

References 22 publications

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

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