Concurrent Multi-Label Prediction in Event Streams

Authors

  • Xiao Shou Rensselaer Polytechnic Institute
  • Tian Gao IBM Research
  • Dharmashankar Subramanian IBM Research
  • Debarun Bhattacharjya IBM Research
  • Kristin P. Bennett Rensselaer Polytechnic Institute

DOI:

https://doi.org/10.1609/aaai.v37i8.26172

Keywords:

ML: Time-Series/Data Streams, DMKM: Data Stream Mining, ML: Graph-based Machine Learning, ML: Multi-Class/Multi-Label Learning & Extreme Classification, RU: Graphical Model

Abstract

Streams of irregularly occurring events are commonly modeled as a marked temporal point process. Many real-world datasets such as e-commerce transactions and electronic health records often involve events where multiple event types co-occur, e.g. multiple items purchased or multiple diseases diagnosed simultaneously. In this paper, we tackle multi-label prediction in such a problem setting, and propose a novel Transformer-based Conditional Mixture of Bernoulli Network (TCMBN) that leverages neural density estimation to capture complex temporal dependence as well as probabilistic dependence between concurrent event types. We also propose potentially incorporating domain knowledge in the objective by regularizing the predicted probability. To represent probabilistic dependence of concurrent event types graphically, we design a two-step approach that first learns the mixture of Bernoulli network and then solves a least-squares semi-definite constrained program to numerically approximate the sparse precision matrix from a learned covariance matrix. This approach proves to be effective for event prediction while also providing an interpretable and possibly non-stationary structure for insights into event co-occurrence. We demonstrate the superior performance of our approach compared to existing baselines on multiple synthetic and real benchmarks.

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Published

2023-06-26

How to Cite

Shou, X., Gao, T., Subramanian, D., Bhattacharjya, D., & Bennett, K. P. (2023). Concurrent Multi-Label Prediction in Event Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9820-9828. https://doi.org/10.1609/aaai.v37i8.26172

Issue

Section

AAAI Technical Track on Machine Learning III