Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection Page: 6
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Collaborative Filtering for BCI
Input: l() initial p)rinmary trailing samliples:
li, t he u111111belI of IewJ )rimary training sam11)les to generate ill Iterat.ion i:
i, tie niaxiiin nmbunl)er of iterations:
a, the milniinim satisfactory classification accuracy:
k, the naxiiiiui k in the kNN classifier:
S. the mlunl)er of classes.
Output: 0,, the optimal A in the kNN classifier.
for i ill [1. i]
for k in [1, k]
Conptite a1, the lcave-onie-oiut cross-validation accuracy using the Np primary santples:
end
m1 = maxk_1 al
if T < a
Compute fa. j = 1. 2..... c. the pe)r-class classification accuracy iii the internal leave-one-olut
cross-validatioin:
Generate ii new primary training samples according to (2):
else
Return k, = arg mna = 1 "
end
end
Return k,, = a.rg max .;_, a
Figure 3. The ACS algorithm, in which the classes from which new training samples are generated are determined based on per-
class cross-validation performance.
doi:1 0.1371/journal.pone.0056624.g003algorithms were applied to the task difficulty level classification
problem introduced in [5]. We first consider kNN because we
have shown that the specific TL and ACS approaches presented in
the previous sections work well with this classifier [30,31]. For
example, in [30] we compared two TL approaches for the kNN
classifier and found that the approach presented in this paper gave
better results; in [31] we compared two ACS approaches for the
kNN classifier and found that the approach presented in this papergave better results. However, the generic framework of combining
TL and ACS should apply to all classifiers, though the
implementation details may differ. To demonstrate this, we also
present results of these methods applied to an SVM classifier.
It is important to note that the purpose of the experiments is not
to show how good a kNN or SVM classifier can be in task difficulty
classification; instead, the goal was to demonstrate how TL and
ACS, and especially their combination, can improve theFigure 4. Methods to combine TL with ACS and AL. Left: Combining TL and ACS; Right: Combining TL and AL.
doi:1 0.1371/journal.pone.0056624.g004February 2013 1 Volume 8 1 Issue 2 1 e56624
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Wu, Dongrui; Lance, Brent J. & Parsons, Thomas D. Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection, article, February 21, 2013; [San Francisco, California]. (https://digital.library.unt.edu/ark:/67531/metadc306986/m1/6/: accessed April 19, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Arts and Sciences.