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Journal article
An AUC-maximizing classifier for skewed and partially labeled data with an application in clinical prediction modeling
Knowledge-based systems, Vol.278, 110831
2023
Appears in Open Access via Read & Publish Agreements
Abstract
Partially labeled and skewed datasets are common in many applications including healthcare, due to the high costs and time constraints of data collection and annotation. However, training machine learning classifiers on such data can undermine their prediction performances. In this paper, we propose a novel classifier to address this problem by focusing on the Area Under the Curve (AUC), which is widely recognized as a more robust performance metric for skewed datasets than other metrics such as accuracy and error rate. We introduce a new classifier called PSVM-AUC Maximizer (PSVM-AUCMax) which is based on Proximal Support Vector Machines (PSVM) and directly maximizes a new AUC-based metric in its learning objective. PSVM-AUCMax has several merits. First, by directly integrating the maximization of the proposed AUC-based metric, PSVM-AUCMax can be proved to have the enhanced generalization capability on the partially labeled and skewed dataset. Second, it simplifies the model selection process with fewer tuning hyperparameters. Third, PSVM-AUCMax’s analytical solution remains the same form as traditional PSVM, preserving its advantages such as fast incremental updating in incremental learning scenarios. The efficacy of PSVM-AUCMax has been demonstrated through extensive experiments on several public datasets and a healthcare case study using data collected at the US Mayo Clinic. In the healthcare case study, we utilized PSVM-AUCMax to develop a clinical prediction model for forecasting composite outcomes in hospitalized COVID-19 patients which yielded promising results.
Details
- Title
- An AUC-maximizing classifier for skewed and partially labeled data with an application in clinical prediction modeling
- Authors/Creators
- Guanjin Wang - Murdoch University, School of Information TechnologyStephen Wai Hang KwokDaniel AxfordMohammed YousufuddinFerdous Sohel - Murdoch University, Centre for Crop and Food Innovation
- Publication Details
- Knowledge-based systems, Vol.278, 110831
- Publisher
- Elsevier B.V.
- Identifiers
- 991005599067407891
- Copyright
- © 2023 The Author(s).
- Murdoch Affiliation
- Harry Butler Institute; School of Information Technology
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.145 Classification Algorithms
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Computer Science