|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Title:||Evolving training sets for improved transfer learning in brain computer interfaces|
|Citation:||Adair J, Brownlee A, Daolio F & Ochoa G (2018) Evolving training sets for improved transfer learning in brain computer interfaces In: Nicosia G, Pardalos P, Giuffrida G, Umeton R (ed.) Machine Learning, Optimization, and Big Data. MOD 2017, Cham, Switzerland: Springer. MOD 2017 - The Third International Conference on Machine Learning, Optimization and Big Data, 14.9.2017 - 17.9.2017, Volterra, Italy, pp. 186-197.|
|Series/Report no.:||Lecture Notes in Computer Science, 10710|
|Abstract:||A new proof-of-concept method for optimising the performance of Brain Computer Interfaces (BCI) while minimising the quantity of required training data is introduced. This is achieved by using an evolutionary approach to rearrange the distribution of training instances, prior to the construction of an Ensemble Learning Generic Information (ELGI) model. The training data from a population was optimised to emphasise generality of the models derived from it, prior to a re-combination with participant-specific data via the ELGI approach, and training of classifiers. Evidence is given to support the adoption of this approach in the more difficult BCI conditions: smaller training sets, and those suffering from temporal drift. This paper serves as a case study to lay the groundwork for further exploration of this approach.|
|Rights:||Publisher policy allows this work to be made available in this repository. Published in: Nicosia G, Pardalos P, Giuffrida G, Umeton R (ed.) Machine Learning, Optimization, and Big Data. MOD 2017, Cham, Switzerland: Springer. MOD 2017 - The Third International Conference on Machine Learning, Optimization and Big Data, 14.9.2017 - 17.9.2017, Volterra, Italy, pp. 186-197. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-72926-8_16|
|evolving-training-sets.pdf||411.17 kB||Adobe PDF||View/Open|
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