A novel structured argumentation framework for improved explainability of classification tasks
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This paper presents a novel framework for structured argumentation, named extend argumentative decision graph (xADG). It is an extension of argumentative decision graphs built upon Dung's abstract argumentation graphs. The xADG framework allows for arguments to use boolean logic operators and multiple premises (supports) within their internal structure, resulting in more concise argumentation graphs that may be easier for users to understand. The study presents a methodology for construction of xADGs and evaluates their size and predictive capacity for classification tasks of varying magnitudes. Resulting xADGs achieved strong (balanced) accuracy, which was accomplished through an input decision tree, while also reducing the average number of supports needed to reach a conclusion. The results further indicated that it is possible to construct plausibly understandable xADGs that outperform other techniques for building ADGs in terms of predictive capacity and overall size. In summary, the study suggests that xADG represents a promising framework to developing more concise argumentative models that can be used for classification tasks and knowledge discovery, acquisition, and refinement.
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