Abstract: The crucial issue in many classification applications is how to
achieve the best possible classifier with a limited number of labeled data for
training. Training data selection is one method which addresses this issue by
selecting the most informative data for training. In this work, we propose
three data selection mechanisms based on fuzzy clustering method: center-based
selection, border-based selection and hybrid selection. Center-based selection
selects the samples with high degree of membership in each cluster as training
data. Border-based selection selects the samples around the border between
clusters. Hybrid selection is the combination of center-based selection and
border-based selection. Compared with existing work, our methods do not require
much computational effort. Moreover, they are independent with respect to the
supervised learning algorithms and initial labeled data. We use fuzzy c-means
to implement our data selection mechanisms. The effects of them are empirically
studied on a set of UCI data sets. Experimental results indicate that, compared
with random selection, hybrid selection can effectively enhance the learning
performance in all the data sets, center-based selection shows better
performance in certain data sets, border-based selection does not show
significant improvement.
