Research on Last State Based Hidden Markov Models Encoding Algorithm

Predictive process monitoring belongs to one of the branches of process mining, which aims to provide information in order to proactively mitigate risks and losses. In this paper, we investigate outcome-oriented predictive process monitoring and propose a new way of sequence encoding. The approach uses Hidden Markov Models to capture the relationship between sequences and outcomes to be added to feature encoding. This method combines Hidden Markov Models with existing methods to reduce the dimensionality of the feature vectors while maintaining effective accuracy. We choose the index-based encoding and the last-state encoding as baseline, while three machine learning algorithms are selected for the experiments. The experiment results proved that our method has effective results.