  In multi-stage processes, decisions occur in an ordered sequence of stages.
Early stages usually have more observations with general information
(easier/cheaper to collect), while later stages have fewer observations but
more specific data. This situation can be represented by a dual funnel
structure, in which the sample size decreases from one stage to the other while
the information increases. Training classifiers in this scenario is challenging
since information in the early stages may not contain distinct patterns to
learn (underfitting). In contrast, the small sample size in later stages can
cause overfitting. We address both cases by introducing a framework that
combines adversarial autoencoders (AAE), multi-task learning (MTL), and
multi-label semi-supervised learning (MLSSL). We improve the decoder of the AAE
with an MTL component so it can jointly reconstruct the original input and use
feature nets to predict the features for the next stages. We also introduce a
sequence constraint in the output of an MLSSL classifier to guarantee the
sequential pattern in the predictions. Using real-world data from different
domains (selection process, medical diagnosis), we show that our approach
outperforms other state-of-the-art methods.
