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Machine Learning and Knowledge Extraction, Volume 3, Issue 1

2021 March - 14 articles

Cover Story: We focus on the main challenges in AI system engineering along the development cycle of machine learning systems including lessons learned from past and ongoing research. This will be done by taking into account intrinsic conditions of deep learning models, data and software quality issues, and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap that superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering, and deployment. The aim of this paper is to pinpoint research topics relevant for AI system engineering by exploring approaches and challenges particularly posed by data quality assurance, embedded AI, confidentiality-preserving transfer learning, human–AI teaming, and ethics by design. View this paper
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Articles (14)

  • Perspective
  • Open Access
37 Citations
10,008 Views
15 Pages

Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big...

(This article belongs to the Section Data)
  • Article
  • Open Access
6 Citations
5,986 Views
21 Pages

Idioms are multi-word expressions whose meaning cannot always be deduced from the literal meaning of constituent words. A key feature of idioms that is central to this paper is their peculiar mixture of fixedness and variability, which poses challeng...

(This article belongs to the Section Data)
  • Article
  • Open Access
8 Citations
5,958 Views
20 Pages

Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging

  • Antoine Pirovano,
  • Hippolyte Heuberger,
  • Sylvain Berlemont,
  • SaÏd Ladjal and
  • Isabelle Bloch

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert’s level, interpretability (highlighting how and what a trained model learned and why it makes a specific...

  • Article
  • Open Access
64 Citations
9,491 Views
15 Pages

The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolu...

(This article belongs to the Section Learning)
  • Article
  • Open Access
13 Citations
4,847 Views
23 Pages

This paper presents a novel on-the-fly, black-box, property-checking through learning approach as a means for verifying requirements of recurrent neural networks (RNN) in the context of sequence classification. Our technique steps on a tool for learn...

(This article belongs to the Special Issue Selected Papers from CD-MAKE 2020 and ARES 2020)
  • Article
  • Open Access
29 Citations
7,979 Views
35 Pages

Explainable AI Framework for Multivariate Hydrochemical Time Series

  • Michael C. Thrun,
  • Alfred Ultsch and
  • Lutz Breuer

The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multi...

  • Article
  • Open Access
3 Citations
6,329 Views
49 Pages

Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM

  • Lars Hillebrand,
  • David Biesner,
  • Christian Bauckhage and
  • Rafet Sifa

Unsupervised topic extraction is a vital step in automatically extracting concise contentual information from large text corpora. Existing topic extraction methods lack the capability of linking relations between these topics which would further help...

(This article belongs to the Special Issue Selected Papers from CD-MAKE 2020 and ARES 2020)
  • Article
  • Open Access
9 Citations
7,531 Views
28 Pages

Learning DOM Trees of Web Pages by Subpath Kernel and Detecting Fake e-Commerce Sites

  • Kilho Shin,
  • Taichi Ishikawa,
  • Yu-Lu Liu and
  • David Lawrence Shepard

The subpath kernel is a class of positive definite kernels defined over trees, which has the following advantages for the purposes of classification, regression and clustering: it can be incorporated into a variety of powerful kernel machines includi...

(This article belongs to the Special Issue Selected Papers from CD-MAKE 2020 and ARES 2020)
  • Article
  • Open Access
16 Citations
7,287 Views
11 Pages

Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors on popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In thi...

(This article belongs to the Section Learning)
  • Feature Paper
  • Article
  • Open Access
9 Citations
4,917 Views
22 Pages

Robust Learning with Implicit Residual Networks

  • Viktor Reshniak and
  • Clayton G. Webster

In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outputs of the proposed implicit residual blocks are defined as the fixed...

(This article belongs to the Special Issue Explainable Machine Learning)
  • Review
  • Open Access
62 Citations
28,952 Views
28 Pages

AI System Engineering—Key Challenges and Lessons Learned

  • Lukas Fischer,
  • Lisa Ehrlinger,
  • Verena Geist,
  • Rudolf Ramler and ,
  • Florian Sobiezky,
  • Werner Zellinger,
  • David Brunner,
  • Mohit Kumar and
  • Bernhard Moser
  • + 4 authors+ 0 author

The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models...

(This article belongs to the Special Issue Selected Papers from CD-MAKE 2020 and ARES 2020)
  • Article
  • Open Access
8 Citations
4,190 Views
20 Pages

Understand Daily Fire Suppression Resource Ordering and Assignment Patterns by Unsupervised Learning

  • Yu Wei,
  • Matthew P. Thompson,
  • Erin J. Belval,
  • David E. Calkin and
  • Jude Bayham

Wildland fire management agencies are responsible for assigning suppression resources to control fire spread and mitigate fire risks. This study implements a principle component analysis and an association rule analysis to study wildland fire respons...

(This article belongs to the Section Learning)
  • Article
  • Open Access
2 Citations
3,838 Views
13 Pages

The Predictive Value of Data from Virtual Investment Communities

  • Benjamin M. Abdel-Karim,
  • Alexander Benlian and
  • Oliver Hinz

Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction mod...

(This article belongs to the Section Data)
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Mach. Learn. Knowl. Extr. - ISSN 2504-4990