Abstract
Most Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of "big data". However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the "human-in-the-loop" approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics.
In this paper, experiments are discussed which help to evaluate the effectiveness of the iML-"human-in-the-loop" approach, particularly in opening the "black box", thereby enabling a human to directly and indirectly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and use it on the Traveling Salesman Problem (TSP) which is of high importance in solving many practical problems in health informatics, e.g. in the study of proteins.
In this paper, experiments are discussed which help to evaluate the effectiveness of the iML-"human-in-the-loop" approach, particularly in opening the "black box", thereby enabling a human to directly and indirectly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and use it on the Traveling Salesman Problem (TSP) which is of high importance in solving many practical problems in health informatics, e.g. in the study of proteins.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science, LNCS 9817 |
Subtitle of host publication | IFIP International Cross Domain Conference and Workshop (CD-ARES 2016) |
Publisher | Springer |
Pages | 81-95 |
DOIs | |
Publication status | Published - 17 Aug 2016 |
Keywords
- Machine Learning
- interactive Machine Learning
- Human-in-the-loop
- Ant-Colony Optimization
- Traveling Salesman Problem
ASJC Scopus subject areas
- Artificial Intelligence
Fields of Expertise
- Information, Communication & Computing
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)