| 11. |
- Gurung, Ram B., et al.
(författare)
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Predicting NOx sensor failure in heavy duty trucks using histogram-based random forests
- 2017
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Ingår i: International Journal of Prognostics and Health Management. - : PHM Society. - 2153-2648. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Being able to accurately predict the impending failures of truck components is often associated with significant amount of cost savings, customer satisfaction and flexibility in maintenance service plans. However, because of the diversity in the way trucks typically are configured and their usage under different conditions, the creation of accurate prediction models is not an easy task. This paper describes an effort in creating such a prediction model for the NOx sensor, i.e., a component measuring the emitted level of nitrogen oxide in the exhaust of the engine. This component was chosen because it is vital for the truck to function properly, while at the same time being very fragile and costly to repair. As input to the model, technical specifications of trucks and their operational data are used. The process of collecting the data and making it ready for training the model via a slightly modified Random Forest learning algorithm is described along with various challenges encountered during this process. The operational data consists of features represented as histograms, posing an additional challenge for the data analysis task. In the study, a modified version of the random forest algorithm is employed, which exploits the fact that the individual bins in the histograms are related, in contrast to the standard approach that would consider the bins as independent features. Experiments are conducted using the updated random forest algorithm, and they clearly show that the modified version is indeed beneficial when compared to the standard random forest algorithm. The performance of the resulting prediction model for the NOx sensor is promising and may be adopted for the benefit of operators of heavy trucks.
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| 12. |
- Gurung, Ram Bahadur, 1983-
(författare)
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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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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 13. |
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| 14. |
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| 15. |
- Hansson, Karin, et al.
(författare)
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Open government ideologies in post-soviet countries
- 2016
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Ingår i: International Journal of Electronic Governance. - 1742-7509 .- 1742-7517. ; 8:3, s. 244-264
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Tidskriftsartikel (refereegranskat)abstract
- Most research in research areas like e-government, e-participation and open government assumes a democratic norm. The open government (OG) concept is commonly based on a general liberal and deliberative ideology emphasising transparency, access, participation and collaboration, but were also innovation and accountability are promoted. In this paper, we outline a terminology and suggest a method for how to investigate the concept more systematically in different policy documents, with a special emphasis on post-soviet countries. The result shows that the main focus in this regions OG policy documents is on freedom of information and accountability, and to a lesser extent on collaboration, while other aspects, such as diversity and innovation, are more rarely mentioned, if at all.
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| 16. |
- Kharazian, Zahra, et al.
(författare)
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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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Ingår i: Advances in Intelligent Data Analysis XXI. - Cham : Springer. - 9783031300462 - 9783031300479 ; , s. 195-207
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Konferensbidrag (refereegranskat)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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| 17. |
- Kharazian, Zahra, et al.
(författare)
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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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Ingår i: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023.
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Konferensbidrag (refereegranskat)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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| 18. |
- Kuratomi Hernandez, Alejandro, et al.
(författare)
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Ijuice : integer JUstIfied counterfactual explanations
- 2024
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Ingår i: Machine Learning. - 0885-6125 .- 1573-0565.
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Tidskriftsartikel (refereegranskat)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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| 19. |
- Kuratomi Hernandez, Alejandro, et al.
(författare)
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JUICE : JUstIfied Counterfactual Explanations
- 2022
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Ingår i: Discovery Science. - Cham : Springer. - 9783031188404 - 9783031188398 ; , s. 493-508
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Konferensbidrag (refereegranskat)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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| 20. |
- Kuratomi Hernandez, Alejandro, et al.
(författare)
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Measuring the Burden of (Un)fairness Using Counterfactuals
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer. - 9783031236174 ; , s. 402-417
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Konferensbidrag (refereegranskat)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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