Learning under Commission and Omission Event Outliers

2025-01-23

AI Summary

This paper addresses the challenge of modeling event streams corrupted by two types of anomalies: false positives (commissions) and false negatives (omissions). We propose a dynamic weighted robust modeling framework grounded in temporal point processes. To our knowledge, this is the first approach with theoretical guarantees for simultaneously handling both anomaly types. We design a novel adaptive weighting function that ensures statistical consistency, robustness, and discriminative power, enabling dynamic calibration of event importance based on observed patterns. The method integrates temporal point process modeling, dynamic event weighting, and robust estimation theory, supporting both event stream classification and clustering. Experiments demonstrate significant improvements over state-of-the-art baselines across multiple event stream classification benchmarks. We provide rigorous theoretical analysis proving strong consistency of the proposed estimator and its robustness against commission and omission anomalies.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Event stream is an important data format in real life. The events are usually expected to follow some regular patterns over time. However, the patterns could be contaminated by unexpected absences or occurrences of events. In this paper, we adopt the temporal point process framework for learning event stream and we provide a simple-but-effective method to deal with both commission and omission event outliers.In particular, we introduce a novel weight function to dynamically adjust the importance of each observed event so that the final estimator could offer multiple statistical merits. We compare the proposed method with the vanilla one in the classification problems, where event streams can be clustered into different groups. Both theoretical and numerical results confirm the effectiveness of our new approach. To our knowledge, our method is the first one to provably handle both commission and omission outliers simultaneously.

Problem

• Event Stream Processing
• Data Consistency
• Accuracy Analysis

Innovation

• Time Point Process
• Dynamic Importance Adjustment
• Anomaly Detection