1 research outputs found
Multi-label learning for dynamic model type recommendation
Dynamic selection techniques aim at selecting the local experts around each
test sample in particular for performing its classification. While generating
the classifier on a local scope may make it easier for singling out the locally
competent ones, as in the online local pool (OLP) technique, using the same
base-classifier model in uneven distributions may restrict the local level of
competence, since each region may have a data distribution that favors one
model over the others. Thus, we propose in this work a problem-independent
dynamic base-classifier model recommendation for the OLP technique, which uses
information regarding the behavior of a portfolio of models over the samples of
different problems to recommend one (or several) of them on a per-instance
manner. Our proposed framework builds a multi-label meta-classifier responsible
for recommending a set of relevant model types based on the local data
complexity of the region surrounding each test sample. The OLP technique then
produces a local pool with the model that yields the highest probability score
of the meta-classifier. Experimental results show that different data
distributions favored different model types on a local scope. Moreover, based
on the performance of an ideal model type selector, it was observed that there
is a clear advantage in choosing a relevant model type for each test instance.
Overall, the proposed model type recommender system yielded a statistically
similar performance to the original OLP with fixed base-classifier model. Given
the novelty of the approach and the gap in performance between the proposed
framework and the ideal selector, we regard this as a promising research
direction. Code available at
github.com/marianaasouza/dynamic-model-recommender.Comment: Paper accepted to the 2020 International Joint Conference on Neural
Network