Title: A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop,
Abstract: The goal of Machine Learning to automatically learn from data, extract
knowledge and to make decisions without any human intervention. Such automatic
(aML) approaches show impressive success. Recent results even demonstrate
intriguingly that deep learning applied for automatic classification of skin
lesions is on par with the performance of dermatologists, yet outperforms the
average. As human perception is inherently limited, such approaches can
discover patterns, e.g. that two objects are similar, in arbitrarily
high-dimensional spaces what no human is able to do. Humans can deal only with
limited amounts of data, whilst big data is beneficial for aML; however, in
health informatics, we are often confronted with a small number of data sets,
where aML suffer of insufficient training samples and many problems are
computationally hard. Here, interactive machine learning (iML) may be of help,
where a human-in-the-loop contributes to reduce the complexity of NP-hard
problems. A further motivation for iML is that standard black-box approaches
lack transparency, hence do not foster trust and acceptance of ML among
end-users. Rising legal and privacy aspects, e.g. with the new European General
Data Protection Regulations, make black-box approaches difficult to use,
because they often are not able to explain why a decision has been made. In
this paper, we present some experiments to demonstrate the effectiveness of the
human-in-the-loop approach, particularly in opening the black-box to a
glass-box and thus enabling a human directly to interact with an learning
algorithm. We selected the Ant Colony Optimization framework, and applied it on
the Traveling Salesman Problem, which is a good example, due to its relevance
for health informatics, e.g. for the study of protein folding. From studies of
how humans extract so much from so little data, fundamental ML-research also
may benefit.