Fuzzy complex number aided evaluation of predictive toxicology models

Xin Fu, K Travis, Daniel Neagu, M Ridley, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


There is a growing interest in applying computational intelligence in the predictive toxicology (PT) domain, where a large number of predictive models are becoming available. Evaluation of such models is therefore considered to be a crucial part of their development and potential use, especially for regulatory purposes. The current evaluation approaches mainly focus on statistical measures of model performance, and few of them have taken data quality into consideration. However, it has been well recognised that datasets and models should not be considered in isolation. This paper proposes a new confidence index for evaluating PT models. A fuzzy complex number (FCN) framework is expanded in an effort to represent and evaluate dataset and regression-based model quality in a two-dimensional manner, thereby ensuring the linguistic evaluation is transparent and explainable. The utility and applicability of this research is illustrated by an experiment which evaluates 17 regression-based PT models. The experimental results have been compared and analysed against existing methods, and show that the FCN-based approach provides a consistent and interpretable means of model assessment. The proposed indexing mechanism can be used, together with customised statistical measures, in assisting PT model selection. This approach also helps to capture the relationships between datasets and models, and contributes to the development of data and model governance in PT.
Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Fuzzy Systems
PublisherIEEE Press
Number of pages8
ISBN (Electronic)978-1-4673-1505-0
ISBN (Print)978-1-4673-1507-4
Publication statusPublished - 2012
EventFuzzy Systems - Queensland, Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012
Conference number: 21


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2012
Period10 Jun 201215 Jun 2012


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