| 1. |
- Bull, L. A., et al.
(author)
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Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning
- 2023
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In: Computer-Aided Civil and Infrastructure Engineering. - : Wiley. - 1093-9687 .- 1467-8667. ; 38:7, s. 821-848
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Journal article (peer-reviewed)abstract
- A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilizing an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different subgroups, representing (1) use-type, (2) component, or (3) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet (15% and 13% increases in predictive log-likelihood of hazard) and power prediction in a wind farm (up to 82% reduction in the standard deviation of maximum output prediction). In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when subfleets are allowed to share correlated information at different levels in the hierarchy; the (averaged) reduction in standard deviation for interpretable parameters in the survival analysis is 70%, alongside 32% in wind farm power models. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e., parameter). Successes in both case studies demonstrate the wide applicability in practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.
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| 2. |
- Dhada, Maharshi, et al.
(author)
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Weibull recurrent neural networks for failure prognosis using histogram data
- 2023
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In: Neural Computing & Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 35:4, s. 3011-3024
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Journal article (peer-reviewed)abstract
- Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network. It offers the combined benefits of the sequential nature of the recurrent neural network, and the ability of the Weibull loss function to incorporate censored data. The goal of this paper is to present the first industrial use case of WTTE-RNN for prognosis. Prognosis of turbocharger conditions in a fleet of heavy-duty trucks is presented here, where the condition data used in the case study were recorded as a time series of sparsely sampled histograms. The experiments include comparison of the prediction models trained using data from the entire fleet of trucks vs data from clustered sub-fleets, where it is concluded that clustering is only beneficial as long as the training dataset is large enough for the model to not overfit. Moreover, the censored data from assets that did not fail are also shown to be incorporated while optimising the Weibull loss function and improve prediction performance. Overall, this paper concludes that WTTE-RNN-based failure predictions enable predictive maintenance policies, which are enhanced by identifying the sub-fleets of similar trucks.
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| 3. |
- Gurung, Ram B., et al.
(author)
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An Interactive Visual Tool Enhance Understanding of Random Forest Prediction
- 2020
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In: Archives of Data Science, Series A. - 2363-9881. ; 6:1
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Journal article (peer-reviewed)abstract
- Random forests are known to provide accurate predictions, but the predictions are not easy to understand. In order to provide support for understanding such predictions, an interactive visual tool has been developed. The tool can be used to manipulate selected features to explore what-if scenarios. It exploits the internal structure of decision trees in a trained forest model and presents these information as interactive plots and charts. In addition, the tool presents a simple decision rule as an explanation for the prediction. It also presents the recommendation for reassignments of feature values of the example that leads to change in the prediction to a preferred class. An evaluation of the tool was undertaken in a large truck manufacturing company, targeting a fault prediction of a selected component in trucks. A set of domain experts were invited to use the tool and provide feedback in post-task interviews. The result of this investigation suggests that the tool indeed may aid in understanding the predictions of random forest, and also allows for gaining new insights.
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| 4. |
- Gurung, Ram Bahadur, 1983-
(author)
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Random Forest for Histogram Data : An application in data-driven prognostic models for heavy-duty trucks
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Data mining and machine learning algorithms are trained on large datasets to find useful hidden patterns. These patterns can help to gain new insights and make accurate predictions. Usually, the training data is structured in a tabular format, where the rows represent the training instances and the columns represent the features of these instances. The feature values are usually real numbers and/or categories. As very large volumes of digital data are becoming available in many domains, the data is often summarized into manageable sizes for efficient handling. To aggregate data into histograms is one means to reduce the size of the data. However, traditional machine learning algorithms have a limited ability to learn from such data, and this thesis explores extensions of the algorithms to allow for more effective learning from histogram data.The thesis focuses on the decision tree and random forest algorithms, which are easy to understand and implement. Although, a single decision tree may not result in the highest predictive performance, one of its benefits is that it often allows for easy interpretation. By combining many such diverse trees into a random forest, the performance can be greatly enhanced, however at the cost of reduced interpretability. By first finding out how to effectively train a single decision tree from histogram data, these findings could be carried over to building robust random forests from such data. The overarching research question for the thesis is: How can the random forest algorithm be improved to learn more effectively from histogram data, and how can the resulting models be interpreted? An experimental approach was taken, under the positivist paradigm, in order to answer the question. The thesis investigates how the standard decision tree and random forest algorithms can be adapted to make them learn more accurate models from histogram data. Experimental evaluations of the proposed changes were carried out on both real world data and synthetically generated experimental data. The real world data was taken from the automotive domain, concerning the operation and maintenance of heavy-duty trucks. Component failure prediction models were built from the operational data of a large fleet of trucks, where the information about their operation over many years have been summarized as histograms. The experimental results showed that the proposed approaches were more effective than the original algorithms, which treat bins of histograms as separate features. The thesis also contributes towards the interpretability of random forests by evaluating an interactive visual tool for assisting users to understand the reasons behind the output of the models.
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| 5. |
- Kharazian, Zahra, et al.
(author)
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AID4HAI : Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning
- 2023
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In: Advances in Intelligent Data Analysis XXI. - Cham : Springer. - 9783031300462 - 9783031300479 ; , s. 195-207
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Conference paper (peer-reviewed)abstract
- This research is an interdisciplinary work between data scientists, innovation management researchers, and experts from a Swedish hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection to control and prevent healthcare-associated infections (HAI). The principal idea of this study is to use machine learning methods to extract informative ideas from social media to assist healthcare professionals in reducing the rate of HAI. Therefore, the proposed package offers a corpus of data collected from Twitter, associated expert-created labels, and software implementation of an annotation framework based on the Active Learning paradigm. We employed Transfer Learning and built a two-step deep neural network model that incrementally extracts the semantic representation of the collected text data using the BERTweet language model in the first step and classifies these representations as informative or non-informative using a multi-layer perception (MLP) in the second step. The package is AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is made fully available (software code and the collected data) through a public GitHub repository (https://github.com/XaraKar/AID4HAI). We believe that sharing our ideas and releasing these ready-to-use tools contributes to the development of the field and inspires future research.
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| 6. |
- Kharazian, Zahra, et al.
(author)
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AID4HAI : Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning
- 2023
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In: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023.
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Conference paper (peer-reviewed)abstract
- This study is a collaboration between data scientists, innovation management researchers from academia, and experts from a hygiene and health company. The study aims to develop an automatic idea detection package to control and prevent healthcare-associated infections (HAI) by extracting informative ideas from social media using Active Learning and Transfer Learning. The proposed package includes a dataset collected from Twitter, expert-created labels, and an annotation framework. Transfer Learning has been used to build a twostep deep neural network model that gradually extracts the semantic representation of the text data using the BERTweet language model in the first step. In the second step, the model classifies the extracted representations as informative or non-informative using a multi-layer perception (MLP). The package is named AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is publicly available on GitHub.
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| 7. |
- Kuratomi Hernandez, Alejandro, et al.
(author)
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Ijuice : integer JUstIfied counterfactual explanations
- 2024
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In: Machine Learning. - 0885-6125 .- 1573-0565.
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Journal article (peer-reviewed)abstract
- Counterfactual explanations modify the feature values of an instance in order to alter its prediction from an undesired to a desired label. As such, they are highly useful for providing trustworthy interpretations of decision-making in domains where complex and opaque machine learning algorithms are utilized. To guarantee their quality and promote user trust, they need to satisfy the faithfulness desideratum, when supported by the data distribution. We hereby propose a counterfactual generation algorithm for mixed-feature spaces that prioritizes faithfulness through k-justification, a novel counterfactual property introduced in this paper. The proposed algorithm employs a graph representation of the search space and provides counterfactuals by solving an integer program. In addition, the algorithm is classifier-agnostic and is not dependent on the order in which the feature space is explored. In our empirical evaluation, we demonstrate that it guarantees k-justification while showing comparable performance to state-of-the-art methods in feasibility, sparsity, and proximity.
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| 8. |
- Kuratomi Hernandez, Alejandro, et al.
(author)
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JUICE : JUstIfied Counterfactual Explanations
- 2022
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In: Discovery Science. - Cham : Springer. - 9783031188404 - 9783031188398 ; , s. 493-508
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Conference paper (peer-reviewed)abstract
- Complex, highly accurate machine learning algorithms support decision-making processes with large and intricate datasets. However, these models have low explainability. Counterfactual explanation is a technique that tries to find a set of feature changes on a given instance to modify the models prediction output from an undesired to a desired class. To obtain better explanations, it is crucial to generate faithful counterfactuals, supported by and connected to observations and the knowledge constructed on them. In this study, we propose a novel counterfactual generation algorithm that provides faithfulness by justification, which may increase developers and users trust in the explanations by supporting the counterfactuals with a known observation. The proposed algorithm guarantees justification for mixed-features spaces and we show it performs similarly with respect to state-of-the-art algorithms across other metrics such as proximity, sparsity, and feasibility. Finally, we introduce the first model-agnostic algorithm to verify counterfactual justification in mixed-features spaces.
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| 9. |
- Kuratomi Hernandez, Alejandro, et al.
(author)
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Measuring the Burden of (Un)fairness Using Counterfactuals
- 2023
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In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer. - 9783031236174 ; , s. 402-417
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Conference paper (peer-reviewed)abstract
- In this paper, we use counterfactual explanations to offer a new perspective on fairness, that, besides accuracy, accounts also for the difficulty or burden to achieve fairness. We first gather a set of fairness-related datasets and implement a classifier to extract the set of false negative test instances to generate different counterfactual explanations on them. We subsequently calculate two measures: the false negative ratio of the set of test instances, and the distance (also called burden) from these instances to their corresponding counterfactuals, aggregated by sensitive feature groups. The first measure is an accuracy-based estimation of the classifier biases against sensitive groups, whilst the second is a counterfactual-based assessment of the difficulty each of these groups has of reaching their corresponding desired ground truth label. We promote the idea that a counterfactual and an accuracy-based fairness measure may assess fairness in a more holistic manner, whilst also providing interpretability. We then propose and evaluate, on these datasets, a measure called Normalized Accuracy Weighted Burden, which is more consistent than only its accuracy or its counterfactual components alone, considering both false negative ratios and counterfactual distance per sensitive feature. We believe this measure would be more adequate to assess classifier fairness and promote the design of better performing algorithms.
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| 10. |
- Kuratomi Hernandez, Alejandro, et al.
(author)
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ORANGE : Opposite-label soRting for tANGent Explanations in heterogeneous spaces
- 2023
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In: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). - : IEEE conference proceedings. - 9798350345032 ; , s. 1-10
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Conference paper (peer-reviewed)abstract
- Most real-world datasets have a heterogeneous feature space composed of binary, categorical, ordinal, and continuous features. However, the currently available local surrogate explainability algorithms do not consider this aspect, generating infeasible neighborhood centers which may provide erroneous explanations. To overcome this issue, we propose ORANGE, a local surrogate explainability algorithm that generates highaccuracy and high-fidelity explanations in heterogeneous spaces. ORANGE has three main components: (1) it searches for the closest feasible counterfactual point to a given instance of interest by considering feasible values in the features to ensure that the explanation is built around the closest feasible instance and not any, potentially non-existent instance in space; (2) it generates a set of neighboring points around this close feasible point based on the correlations among features to ensure that the relationship among features is preserved inside the neighborhood; and (3) the generated instances are weighted, firstly based on their distance to the decision boundary, and secondly based on the disagreement between the predicted labels of the global model and a surrogate model trained on the neighborhood. Our extensive experiments on synthetic and public datasets show that the performance achieved by ORANGE is best-in-class in both explanation accuracy and fidelity.
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