Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach - Availability, Reliability, and Security in Information Systems Access content directly
Conference Papers Year : 2016

Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

Andreas Holzinger
  • Function : Author
  • PersonId : 1022786
Markus Plass
  • Function : Author
  • PersonId : 1022815
Katharina Holzinger
  • Function : Author
  • PersonId : 1022816
Camelia-M. Pintea
  • Function : Author
  • PersonId : 1022818
Vasile Palade
  • Function : Author
  • PersonId : 1022819

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.
Fichier principal
Vignette du fichier
430962_1_En_6_Chapter.pdf (956.51 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01635020 , version 1 (14-11-2017)

Licence

Attribution

Identifiers

Cite

Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crişan, Camelia-M. Pintea, et al.. Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach. International Conference on Availability, Reliability, and Security (CD-ARES), Aug 2016, Salzburg, Austria. pp.81-95, ⟨10.1007/978-3-319-45507-5_6⟩. ⟨hal-01635020⟩
272 View
902 Download

Altmetric

Share

Gmail Facebook X LinkedIn More