# Binary Classification with Positive Labeling Sources

arXiv: https://arxiv.org/abs/2208.01704 · HTML: https://mlsanity.com/paper/2208.01704/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach](https://mlsanity.com/paper/2010.07835/) (2020)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale](https://mlsanity.com/paper/1812.00417/) (2018)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [Denoising Multi-Source Weak Supervision for Neural Text Classification](https://mlsanity.com/paper/2010.04582/) (2020)
- [Cross-Modal Data Programming Enables Rapid Medical Machine Learning](https://mlsanity.com/paper/1903.11101/) (2019)
- [Scene Graph Prediction with Limited Labels](https://mlsanity.com/paper/1904.11622/) (2019)
- [SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data](https://mlsanity.com/paper/1704.06360/) (2017)
- [NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction](https://mlsanity.com/paper/1909.02177/) (2019)
- [Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News](https://mlsanity.com/paper/2004.01732/) (2020)
- [Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling](https://mlsanity.com/paper/1910.04289/) (2019)
- [GOGGLES: Automatic Image Labeling with Affinity Coding](https://mlsanity.com/paper/1903.04552/) (2019)
- [Ontology-driven weak supervision for clinical entity classification in electronic health records](https://mlsanity.com/paper/2008.01972/) (2020)
- [Multi-Resolution Weak Supervision for Sequential Data](https://mlsanity.com/paper/1910.09505/) (2019)
- [Learning with Weak Supervision for Email Intent Detection](https://mlsanity.com/paper/2005.13084/) (2020)
- [BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2105.12848/) (2021)
- [Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels](https://mlsanity.com/paper/1910.02993/) (2019)
- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach](https://mlsanity.com/paper/1707.00166/) (2017)
- [Understanding Programmatic Weak Supervision via Source-aware Influence Function](https://mlsanity.com/paper/2205.12879/) (2022)

## Built on by (most cited first, 2 of 2)

- [Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision](https://mlsanity.com/paper/2210.02724/) (2022)
- [WeShap: Weak Supervision Source Evaluation with Shapley Values](https://mlsanity.com/paper/2406.11010/) (2024)

## Abstract (arXiv)

To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic labeling sources rather than manual annotation. Existing works of WS for binary classification typically assume the presence of labeling sources that are able to assign both positive and negative labels to data in roughly balanced proportions. However, for many tasks of interest where there is a minority positive class, negative examples could be too diverse for developers to generate indicative labeling sources. Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. We propose WEAPO, a simple yet competitive WS method for producing training labels without negative labeling sources. On 10 benchmark datasets, we show WEAPO achieves the highest averaged performance in terms of both the quality of synthesized labels and the performance of the final classifier supervised with these labels. We incorporated the implementation of method into WRENCH, an existing benchmarking platform.