  Semi-supervised learning (SSL) algorithms struggle to perform well when
exposed to imbalanced training data. In this scenario, the generated
pseudo-labels can exhibit a bias towards the majority class, and models that
employ these pseudo-labels can further amplify this bias. Here we investigate
pseudo-labeling strategies for imbalanced SSL including pseudo-label refinement
and threshold adjustment, through the lens of statistical analysis. We find
that existing SSL algorithms which generate pseudo-labels using heuristic
strategies or uncalibrated model confidence are unreliable when imbalanced
class distributions bias pseudo-labels. To address this, we introduce
SEmi-supervised learning with pseudo-label optimization based on VALidation
data (SEVAL) to enhance the quality of pseudo-labelling for imbalanced SSL. We
propose to learn refinement and thresholding parameters from a partition of the
training dataset in a class-balanced way. SEVAL adapts to specific tasks with
improved pseudo-labels accuracy and ensures pseudo-labels correctness on a
per-class basis. Our experiments show that SEVAL surpasses state-of-the-art SSL
methods, delivering more accurate and effective pseudo-labels in various
imbalanced SSL situations. SEVAL, with its simplicity and flexibility, can
enhance various SSL techniques effectively. The code is publicly
available~\footnote{\url{https://github.com/ZerojumpLine/SEVAL}}.
