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Machine Learning and Knowledge Extraction [electronic resource] : 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, Vienna, Austria, August 23-26, 2022, Proceedings / edited by Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl.

Contributor(s): Holzinger, Andreas [editor.] | Kieseberg, Peter [editor.] | Tjoa, A Min [editor.] | Weippl, Edgar [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 13480Publisher: Cham : Springer International Publishing : Imprint: Springer, 2022Edition: 1st ed. 2022.Description: XIII, 378 p. 130 illus., 119 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031144639.Subject(s): Artificial intelligence | Software engineering | Database management | Data mining | Information storage and retrieval systems | Machine theory | Artificial Intelligence | Software Engineering | Database Management | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Formal Languages and Automata TheoryAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Explain to Not Forget: Defending Catastrophic Forgetting with XAI -- Approximation of SHAP values for Randomized Tree Ensembles -- Color shadows (part I): exploratory usability evaluation of activation maps in radiological machine learning -- Effects of Fairness and Explanation on Trust in Ethical AI -- Towards Refined Classifications driven by SHAP explanations -- Global Intepretable Calibration Index, a New Metric to Estimate Machine Learning Models' Calibration -- The ROC Diagonal is not Layperson's Chance: a New Baseline Shows the Useful Area -- Debiasing MDI Feature Importance and SHAP values in Tree Ensembles -- The Influence of User Diversity on Motives and Barriers when Using Health Apps - A Conjoint Investigation of the Intention-Behavior Gap -- Identifying Fraud Rings Using Domain Aware Weighted Community Detection -- Capabilities, limitations and challenges of style transfer with CycleGANs: a study on automatic ring design generation -- Semantic Causal Abstraction for Event Prediction -- An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments -- Towards Generating Financial Reports From Tabular Data Using Transformers -- Evaluating the performance of SOBEK text mining keyword extraction algorithm -- Classification of Screenshot Image Captured in Online Meeting System -- A survey on the application of virtual reality in event-related potential research -- Visualizing Large Collections of URLs Using the Hilbert Curve -- How to Reduce the Time Necessary for Evaluation of Tree-based Models -- An Empirical Analysis of and Guidelines for Synthetic-Data-based Anomaly Detection -- SECI Model in Data-Based Procedure for the Assessment of the Frailty State in Diabetic Patients -- Comparing machine learning correlations to domain experts' causal knowledge: Employee turnover use case -- Machine learning and knowledge extraction to support work safety for smart forest operations.
In: Springer Nature eBookSummary: This book constitutes the refereed proceedings of the 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, held in Vienna, Austria during August 2022. The 23 full papers presented were carefully reviewed and selected from 45 submissions. The papers are covering a wide range from integrative machine learning approach, considering the importance of data science and visualization for the algorithmic pipeline with a strong emphasis on privacy, data protection, safety and security.
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Explain to Not Forget: Defending Catastrophic Forgetting with XAI -- Approximation of SHAP values for Randomized Tree Ensembles -- Color shadows (part I): exploratory usability evaluation of activation maps in radiological machine learning -- Effects of Fairness and Explanation on Trust in Ethical AI -- Towards Refined Classifications driven by SHAP explanations -- Global Intepretable Calibration Index, a New Metric to Estimate Machine Learning Models' Calibration -- The ROC Diagonal is not Layperson's Chance: a New Baseline Shows the Useful Area -- Debiasing MDI Feature Importance and SHAP values in Tree Ensembles -- The Influence of User Diversity on Motives and Barriers when Using Health Apps - A Conjoint Investigation of the Intention-Behavior Gap -- Identifying Fraud Rings Using Domain Aware Weighted Community Detection -- Capabilities, limitations and challenges of style transfer with CycleGANs: a study on automatic ring design generation -- Semantic Causal Abstraction for Event Prediction -- An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments -- Towards Generating Financial Reports From Tabular Data Using Transformers -- Evaluating the performance of SOBEK text mining keyword extraction algorithm -- Classification of Screenshot Image Captured in Online Meeting System -- A survey on the application of virtual reality in event-related potential research -- Visualizing Large Collections of URLs Using the Hilbert Curve -- How to Reduce the Time Necessary for Evaluation of Tree-based Models -- An Empirical Analysis of and Guidelines for Synthetic-Data-based Anomaly Detection -- SECI Model in Data-Based Procedure for the Assessment of the Frailty State in Diabetic Patients -- Comparing machine learning correlations to domain experts' causal knowledge: Employee turnover use case -- Machine learning and knowledge extraction to support work safety for smart forest operations.

This book constitutes the refereed proceedings of the 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, held in Vienna, Austria during August 2022. The 23 full papers presented were carefully reviewed and selected from 45 submissions. The papers are covering a wide range from integrative machine learning approach, considering the importance of data science and visualization for the algorithmic pipeline with a strong emphasis on privacy, data protection, safety and security.

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