39th German Conference on Artificial Intelligence

Holzinger, A. (Speaker)

Activity: Talk or presentationTalk at workshop, seminar or courseScience to science

Description

Towards interactive Machine Learning for solving complex problems in Health Informatics In automatic machine learning (aML) great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in health informatics, we are often confronted with a small number of data sets or rare events, where aML suffer of insufficient training samples. Here interactive Machine Learning (iML) may be of help, having its roots in reinforcement learning, preference learning and active learning. The term iML is not yet well used, so we define it as algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human. This human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase. For the successful application of ML in health informatics a multidisciplinary skill set is required, encompassing the following seven specializations: 1) data science, 2) algorithms, 3) network science, 4) graphs/topology, 5) time/entropy, 6) data visualization, and 7) privacy, data protection, safety and security, fostered in the HCI-KDD approach. After giving a very brief introduction to the HCI-KDD approach, I start this talk with showing some problems with probabilistic information in the health domain. After the definition of aML and showing some examples I will provide some insight into our latest iML-research.
Period27 Sep 2016
Held at39th German Conference on Artificial Intelligence
Event typeWorkshop
LocationKlagenfurt, Austria

Keywords

  • Machine Learning
  • Health Informatics
  • Probabilistic Programming
  • Artificial Intelligence
  • Information, Communication & Computing
  • Basic - Fundamental (Grundlagenforschung)