  Semi-supervised support vector machines (S3VMs) are a kind of popular
approaches which try to improve learning performance by exploiting unlabeled
data. Though S3VMs have been found helpful in many situations, they may
degenerate performance and the resultant generalization ability may be even
worse than using the labeled data only. In this paper, we try to reduce the
chance of performance degeneration of S3VMs. Our basic idea is that, rather
than exploiting all unlabeled data, the unlabeled instances should be selected
such that only the ones which are very likely to be helpful are exploited,
while some highly risky unlabeled instances are avoided. We propose the
S3VM-\emph{us} method by using hierarchical clustering to select the unlabeled
instances. Experiments on a broad range of data sets over eighty-eight
different settings show that the chance of performance degeneration of
S3VM-\emph{us} is much smaller than that of existing S3VMs.
