Explainable AI Framework for Multivariate Hydrochemical Time Series

Thrun, Michael C.; Ultsch, Alfred

doi:10.20944/preprints202011.0451.v1

Cited by 4 publications

(4 citation statements)

References 61 publications

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“…A clustering structure was not reported in [71]. Additionally, an explainable AI procedure [75] explained the clustering by one rule stating that the taxes were lower in cluster one than in cluster two.…”

Section: Discussionmentioning

confidence: 98%

“…Additionally, The Welch two sample t-test indicates that cluster one has greater NOX (air pollution) values than cluster two ((t = − 21.342, df = 401, p-value < 2.2e−16)). Adapting the XAI procedure described in [75] to this dataset, the clustering can be explained by one rule stating that TAX values below 568 define cluster one and cluster TAX values above 558 cluster two with an accuracy of 100%.…”

Section: Application: Boston Housingmentioning

confidence: 99%

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Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Thrun

Pape

Ultsch

2021

Int J Data Sci Anal

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Clustering is an important task in knowledge discovery with the goal to identify structures of similar data points in a dataset. Here, the focus lies on methods that use a human-in-the-loop, i.e., incorporate user decisions into the clustering process through 2D and 3D displays of the structures in the data. Some of these interactive approaches fall into the category of visual analytics and emphasize the power of such displays to identify the structures interactively in various types of datasets or to verify the results of clustering algorithms. This work presents a new method called interactive projection-based clustering (IPBC). IPBC is an open-source and parameter-free method using a human-in-the-loop for an interactive 2.5D display and identification of structures in data based on the user’s choice of a dimensionality reduction method. The IPBC approach is systematically compared with accessible visual analytics methods for the display and identification of cluster structures using twelve clustering benchmark datasets and one additional natural dataset. Qualitative comparison of 2D, 2.5D and 3D displays of structures and empirical evaluation of the identified cluster structures show that IPBC outperforms comparable methods. Additionally, IPBC assists in identifying structures previously unknown to domain experts in an application.

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Section: Discussionmentioning

confidence: 98%

Section: Application: Boston Housingmentioning

confidence: 99%

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Thrun

Pape

Ultsch

2021

Int J Data Sci Anal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, model-agnostic methods have attracted a lot of attention for feature evaluation, such as Shapley Additive Explanation (SHAP) [34] and LIME [28]. Explainable AI techniques in general have been widely used to explain predictions in financial and chemical time-series data [77,78,79,80] vibrational-based Structural Health Monitoring signals [50], hyperspectral imaging [81] and electrocardiogram data [82]. However, to the best of our knowledge, only one recent work focused on using the model-agnostic method (LIME) to explain the non-linear predictions of spectroscopy data to characterize plasma solution conductivity [29].…”

Section: Related Workmentioning

confidence: 99%

Explainable Predictive Modeling for Limited Spectral Data

Akulich¹,

Anahideh²,

Sheyyab³

et al. 2022

Preprint

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Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain experts. Spectroscopy data, which records the interaction between matter and electromagnetic radiation, particularly holds a lot of information in a single sample. Since acquiring such high-dimensional data is a complex task, it is crucial to exploit the best analytical tools to extract necessary information. In this paper, we investigate the most commonly used feature selection techniques and introduce applying recent explainable AI techniques to interpret the prediction outcomes of high-dimensional and limited spectral data. Interpretation of the prediction outcome is beneficial for the domain experts as it ensures the transparency and faithfulness of the ML models to the domain knowledge. Due to the instrument resolution limitations, pinpointing important regions of the spectroscopy data creates a pathway to optimize the data collection process through the miniaturization of the spectrometer device. Reducing the device size and power and therefore cost is a requirement for the real-world deployment of such a sensor-to-prediction system as a whole. Furthermore, we consider a wide range of machine learning models that have been proven to be successful for the prediction of the Cetane Number of fuels. We specifically design three different scenarios to ensure that the evaluation of ML models is robust for the real-time practice of the developed methodologies and to uncover the hidden effect of noise sources on the final outcome. The evaluation is performed for both the full model and reduced models using different feature selection techniques on a real dataset. Finally, we propose a correctness metric for the feature selection techniques to assess the conformance of the selected subset

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“…ANNs were used to predict the root-mean-square pressure coefficients of the buildings and the wind-induced pressure at different time intervals on various structures [ 39 , 40 ]. MLP and decision trees were implemented to analyze the nonlinear relationship between the various environmental factors for predicting the deformation of the unstable slopes [ 41 ]. Convolutional neural network (CNN) is a deep learning approach which is mostly applied for image-related applications.…”

Section: Related Workmentioning

confidence: 99%

Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network

Kim

Yuvaraj

Preethaa

et al. 2021

Sensors

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Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings. However, some pressure sensors usually fail and cause loss of data, which are difficult to restore. In the literature, numerous techniques are implemented for imputing the single instance data values and data imputation for multiple instantaneous time intervals with accurate predictions needs to be addressed. Thus, the data imputation capacity of machine learning models is used to predict the missing wind pressure data for tall buildings in this study. A generative adversarial imputation network (GAIN) is proposed to predict the pressure coefficients at various instantaneous time intervals on tall buildings. The proposed model is validated by comparing the performance of GAIN with that of the K-nearest neighbor and multiple imputations by chained equation models. The experimental results show that the GAIN model provides the best fit, achieving more accurate predictions with the minimum average variance and minimum average standard deviation. The average mean-squared error for all four sides of the building was the minimum (0.016), and the average R-squared error was the maximum (0.961). The proposed model can ensure the health and prolonged existence of a structure based on wind environment.

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Explainable AI Framework for Multivariate Hydrochemical Time Series

Cited by 4 publications

References 61 publications

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Explainable Predictive Modeling for Limited Spectral Data

Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network

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