Towards Interactive Machine Learning for solving complex problems

Beschreibung

The goal of Machine Learning (ML) is to develop algorithms which can learn from data and improve over time. 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. Sometimes, for example, in the biomedical domain, or in production engineering, we are confronted with rare events and a small number of data sets, where aML-approaches suffer of insufficient training samples. In such domains we are confronted with uncertainties and non-determinism on which automatic algorithms can not easily be applied. Here, interactive Machine Learning (iML) may be of help, defined as "algorithms that can interact with agents and can optimize their learning behaviour through these interactions, where the agents can also be human". For example, an expert-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. However, for the successful application of ML in the biomedical domain 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.

Zeitraum	13 Juli 2016
Gehalten am	Fraunhofer IOSB, Deutschland