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Interpretable And Anti-Bias Machine Learning Models For Human Event Sequence Data, Zihan Zhou

LSU Doctoral Dissertations

Growing volumes and varieties of human event sequence data are available in many applications such as recommender systems, social network, medical diagnosis, and predictive policing. Human event sequence data is usually clustered and exhibits self-exciting properties. Machine learning models especially deep neural network models have shown great potential in improving the prediction accuracy of future events. However, current approaches still suffer from several drawbacks such as model transparency, unfair prediction and the poor prediction accuracy due to data sparsity and bias. Another issue in modeling human event data is that data collected from real word is usually incomplete, and even …