© 2017

Towards Integrative Machine Learning and Knowledge Extraction

BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers

  • Andreas Holzinger
  • Randy Goebel
  • Massimo Ferri
  • Vasile Palade
Conference proceedings

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10344)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10344)

Table of contents

  1. Front Matter
    Pages I-XVI
  2. Andreas Holzinger, Randy Goebel, Vasile Palade, Massimo Ferri
    Pages 1-12
  3. Andreas Holzinger, Bernd Malle, Peter Kieseberg, Peter M. Roth, Heimo Müller, Robert Reihs et al.
    Pages 13-50
  4. Arnaud Nguembang Fadja, Fabrizio Riguzzi
    Pages 89-116
  5. Vincenzo Manca
    Pages 146-149
  6. Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla
    Pages 150-169
  7. V. A. Baikov, R. R. Gilmanov, I. A. Taimanov, A. A. Yakovlev
    Pages 182-193
  8. Deepika Singh, Erinc Merdivan, Sten Hanke, Johannes Kropf, Matthieu Geist, Andreas Holzinger
    Pages 194-205
  9. Back Matter
    Pages 207-207

About these proceedings


The BIRS Workshop “Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets” (15w2181), held in July 2015 in Banff, Canada, was dedicated to stimulating a cross-domain integrative machine-learning approach and appraisal of “hot topics” toward tackling the grand challenge of reaching a level of useful and useable computational intelligence with a focus on real-world problems, such as in the health domain. This encompasses learning from prior data, extracting and discovering knowledge, generalizing the results, fighting the curse of dimensionality, and ultimately disentangling the underlying explanatory factors in complex data, i.e., to make sense of data within the context of the application domain. 

The workshop aimed to contribute advancements in promising novel areas such as at the intersection of machine learning and topological data analysis. History has shown that most often the overlapping areas at intersections of seemingly disparate fields are key for the stimulation of new insights and further advances. This is particularly true for the extremely broad field of machine learning.


artificial intelligence bioinformatics brain tumor classification data mining decision trees deep learning digital pathology health informatics image processing information theory integrative machine learning knowledge extraction learning algorithms persistent homology predictive models probabilistic logic progamming

Editors and affiliations

  1. 1.Medical University GrazGrazAustria
  2. 2.University of AlbertaEdmontonCanada
  3. 3.Bologna UniversityBolognaItaly
  4. 4.Coventry UniversityCoventryUnited Kingdom

Bibliographic information

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