A Novel Approach to Semi-Supervised Statistical Machine Learning

半监督统计机器学习的新方法

• 批准号: DP230101671
• 负责人: Prof Geoffrey McLachlan
• 金额: $ 29.08万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230101671
• 关键词: Novel; Approach; Semi; Supervised; Statistical

Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount importance to applications where it is expensive or impractical to obtain much labelled data. The project is to develop a novel SSL approach that adopts a missingness mechanism for the missing labels to build a classifier that not only improves accuracy but it can be greater than if the missing labels were known.

最近在构建用于诊断和预测的分类器方面取得了成功，部分原因是它们使用了大量标记为其起源类的数据。但通常情况下，有标签的数据很少，但没有标签的数据却很多。半监督学习（SSL）的目标是利用大量未标记的数据来仅使用小的标记数据集来提高性能，因此SSL对于获取大量标记数据昂贵或不切实际的应用程序至关重要。该项目旨在开发一种新颖的SSL方法，该方法采用缺失标签的缺失机制来构建分类器，该分类器不仅提高了准确性，而且比已知缺失标签的分类器更准确。