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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 butmore specific data. This situation can be represented by a dual funnelstructure, in which the sample size decreases from one stage to the other whilethe information increases. Training classifiers in this scenario is challengingsince information in the early stages may not contain distinct patterns tolearn (underfitting). In contrast, the small sample size in later stages cancause overfitting. We address both cases by introducing a framework thatcombines adversarial autoencoders (AAE), multi-task learning (MTL), andmulti-label semi-supervised learning (MLSSL). We improve the decoder of the AAEwith an MTL component so it can jointly reconstruct the original input and usefeature nets to predict the features for the next stages. We also introduce asequence constraint in the output of an MLSSL classifier to guarantee thesequential pattern in the predictions. Using real-world data from differentdomains (selection process, medical diagnosis), we show that our approachoutperforms other state-of-the-art methods.

Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes