Interpretable Machine leArninG models for bio and chemoINinformatics and medicinE

(IMAGINE 2020)

as part of

The 20th IEEE International Conference on Data Mining
(ICDM 2020)

Paper submission is 24th August, 2020

Call for Papers
Important dates

Science is about understanding! Although Data Mining (DM) has been extremely useful in a large number of domains where data analysis is necessary, most of the concerns of the DM users has been the development of highly accurate models according to some pre-specified metrics. Those concerns are completely justified and enough for a wide range applications like predicting stock exchange market, for example. However, in Scientific applications like the Live Sciences or Chemistry a good performance according to the evaluation metric is most often not enough and an explanation for the phenomena that produced the data is required. DM algorithms that produce Symbolic models, or approaches that extract useful knowledge from black-box models are very useful tools to propose explanations that could help experts to better understand the domain. This workshop addresses such concerns. The workshop will be concerned with the exchange of experience among researchers and provide updated knowledge concerning the extraction of useful domain knowledge either by directly using symbolic DM algorithms or post-processing non-symbolic ones.

The topics of interest include (but are not restricted to) the following ones:

  • Novel symbolic Machine Learning algorithms
  • Novel post-processing approaches to extract knowledgeable information from black-box systems
  • Applications of symbolic data mining systems in Bioinformatics
  • Applications of symbolic data mining systems in Chemoinformatics
  • Data Mining support tools to obtain intelligible knowledge from data

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