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  • 1.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Ståhl, Niclas
    Jönköping University, School of Engineering, JTH, Department of Computing.
    Well-Calibrated Rule Extractors2022In: Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications: Volume 179: Conformal and Probabilistic Prediction with Applications, 24-26 August 2022, Brighton, UK / [ed] U. Johansson, H. Boström, K. A. Nguyen, Z. Luo & L. Carlsson, ML Research Press , 2022, Vol. 179, p. 72-91Conference paper (Refereed)
    Abstract [en]

    While explainability is widely considered necessary for trustworthy predictive models, most explanation modules give only a limited understanding of the reasoning behind the predictions. In pedagogical rule extraction, an opaque model is approximated with a transparent model induced using original training instances, but with the predictions from the opaque model as targets. The result is an interpretable model revealing the exact reasoning used for every possible prediction. The pedagogical approach can be applied to any opaque model and use any learning algorithm producing transparent models as the actual rule extractor. Unfortunately, even if the extracted model is induced to mimic the opaque, test set fidelity may still be poor, thus clearly limiting the value of using the extracted model for explanations and analyses. In this paper, it is suggested to alleviate this problem by extracting probabilistic predictors with well-calibrated fitness estimates. For the calibration, Venn-Abers with its unique validity guarantees, is employed. Using a setup where decision trees are extracted from MLP neural networks, the suggested approach is first demonstrated in detail on one real-world data set. After that, a large-scale empirical evaluation using 25 publicly available benchmark data sets is presented. The results show that the method indeed extracts interpretable models with well-calibrated fitness estimates, i.e., the extracted model can be used for explaining the opaque. Specifically, in the setup used, every leaf in a decision tree contains a label and a well-calibrated probability interval for the fidelity. Consequently, a user could, in addition to obtaining explanations of individual predictions, find the parts of feature space where the decision tree is a good approximation of the MLP and not. In fact, using the sizes of the probability intervals, the models also provide an indication of how certain individual fitness estimates are.

  • 2.
    Ståhl, Niclas
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Bae, Juhee
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Utilising Data from Multiple Production Lines for Predictive Deep Learning Models2022In: Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference / [ed] Kenji Matsui; Sigeru Omatu; Tan Yigitcanlar; Sara Rodríguez González, Cham: Springer , 2022, p. 67-76Conference paper (Refereed)
    Abstract [en]

    A Basic Oxygen Furnace (BOF) for steel making is a complex industrial process that is difficult to monitor due to the harsh environment, so the collected production data is very limited given the process complexity. Also, such production data has a low degree of variability. An accurate machine learning (ML) model for predicting production outcome requires both large and varied data, so utilising data from multiple BOFs will allow for more capable ML models, since both the amount and variability of data increases. Data collection setups for different BOFs are different, such that data sets are not compatible to directly join for ML training. Our approach is to let a neural network benefit from these collection differences in a joint training model. We present a neural network-based approach that simultaneously and jointly co-trains on several data sets. Our novelty is that the first network layer finds an internal representation of each individual BOF, while the other layers use this representation to concurrently learn a common BOF model. Our evaluation shows that the prediction accuracy of the common model increases compared to separate models trained on individual furnaces’ data sets. It is clear that multiple data sets can be utilised this way to increase model accuracy for better production prediction performance. For the industry, this means that the amount of available data for model training increases and thereby more capable ML models can be trained when having access to multiple data sets describing the same or similar manufacturing processes. 

  • 3.
    Ståhl, Niclas
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing. Skövde Artificial Intelligence Lab, University of Skövde, Skövde, Sweden.
    Weimann, Lisa
    County Administrative Board of Jönköping, Sweden.
    Identifying wetland areas in historical maps using deep convolutional neural networks2022In: Ecological Informatics, ISSN 1574-9541, E-ISSN 1878-0512, Vol. 68, article id 101557Article in journal (Refereed)
    Abstract [en]

    The local environment and land usages have changed a lot during the past one hundred years. Historical documents and materials are crucial in understanding and following these changes. Historical documents are, therefore, an important piece in the understanding of the impact and consequences of land usage change. This, in turn, is important in the search of restoration projects that can be conducted to turn and reduce harmful and unsustainable effects originating from changes in the land-usage.

    This work extracts information on the historical location and geographical distribution of wetlands, from hand-drawn maps. This is achieved by using deep learning (DL), and more specifically a convolutional neural network (CNN). The CNN model is trained on a manually pre-labelled dataset on historical wetlands in the area of Jönköping county in Sweden. These are all extracted from the historical map called “Generalstabskartan”.

    The presented CNN performs well and achieves a F1-score of 0.886 when evaluated using a 10-fold cross validation over the data. The trained models are additionally used to generate a GIS layer of the presumable historical geographical distribution of wetlands for the area that is depicted in the southern collection in Generalstabskartan, which covers the southern half of Sweden. This GIS layer is released as an open resource and can be freely used.

    To summarise, the presented results show that CNNs can be a useful tool in the extraction and digitalisation of non-textual information in historical documents, such as historical maps. A modern GIS material that can be used to further understand the past land-usage change is produced within this research. Previously, no material of this detail and extent have been available, due to the large effort needed to manually create such. However, with the presented resource better quantifications and estimations of historical wetlands that have been lost can be made.

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