Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning

• URL: http://arxiv.org/abs/2404.05809v1
• Date: Mon, 8 Apr 2024 18:16:22 GMT
• Title: Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning
• Authors: Yutian Ren, Aaron Haohua Yen, G. P. Li,
• Abstract summary: An interactive causality based self-labeling method was proposed to autonomously associate causally related data streams for domain adaptation. This paper further develops the self-labeling framework and its theoretical foundations to address these research questions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive machine learning (ML) aims to allow ML models to adapt to ever-changing environments with potential concept drift after model deployment. Traditionally, adaptive ML requires a new dataset to be manually labeled to tailor deployed models to altered data distributions. Recently, an interactive causality based self-labeling method was proposed to autonomously associate causally related data streams for domain adaptation, showing promising results compared to traditional feature similarity-based semi-supervised learning. Several unanswered research questions remain, including self-labeling's compatibility with multivariate causality and the quantitative analysis of the auxiliary models used in the self-labeling. The auxiliary models, the interaction time model (ITM) and the effect state detector (ESD), are vital to the success of self-labeling. This paper further develops the self-labeling framework and its theoretical foundations to address these research questions. A framework for the application of self-labeling to multivariate causal graphs is proposed using four basic causal relationships, and the impact of non-ideal ITM and ESD performance is analyzed. A simulated experiment is conducted based on a multivariate causal graph, validating the proposed theory.

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