Machine learning (ML) is the fastest growing field in computer science and the grand goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Consequently, most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example: in speech recognition, recommender systems, or autonomous vehicles (“Google Car”). Automatic approaches greatly benefit from big data with many training sets. However, in the health domain,
sometimes we are confronted with a small number of data sets or rare events, and complex problems, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, defined 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’’ or taking swarm intelligence into play: “humans-in-the-loop”, can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization, 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 human agents involved in the learning phase.