The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time

TL;DR

This paper provides a theoretical analysis of fixed-segment weak labeling for event detection, examining how segment length impacts label accuracy and annotation cost, and comparing it to an oracle method for improved efficiency.

Contribution

It introduces a theoretical framework for understanding the accuracy and cost trade-offs in fixed-segment weak labeling and highlights the benefits of adaptive strategies over fixed-length approaches.

Findings

Oracle method outperforms fixed-length labeling in accuracy and cost in most scenarios.

Segment length significantly influences label accuracy and annotation effort.

Adaptive weak labeling strategies can approximate the oracle's performance.

Abstract

Accurate labels are critical for deriving robust machine learning models. Labels are used to train supervised learning models and to evaluate most machine learning paradigms. In this paper, we model the accuracy and cost of a common weak labeling process where annotators assign presence or absence labels to fixed-length data segments for a given event class. The annotator labels a segment as "present" if it sufficiently covers an event from that class, e.g., a birdsong sound event in audio data. We analyze how the segment length affects the label accuracy and the required number of annotations, and compare this fixed-length labeling approach with an oracle method that uses the true event activations to construct the segments. Furthermore, we quantify the gap between these methods and verify that in most realistic scenarios the oracle method is better than the fixed-length labeling method in both accuracy and cost. Our findings provide a theoretical justification for adaptive weak labeling strategies that mimic the oracle process, and a foundation for optimizing weak labeling processes in sequence labeling tasks.