Javascript must be enabled for the correct page display

Thesis: Grounded knowledge acquisition by argumentation, an implementation for fraud detection

Rooij, P.N. de (2017) Thesis: Grounded knowledge acquisition by argumentation, an implementation for fraud detection. Master's Thesis / Essay, Artificial Intelligence.

Master_AI_ResearchProject_2017_1.pdf - Published Version

Download (5MB) | Preview
[img] Text
toestemming.pdf - Other
Restricted to Backend only

Download (79kB)


Machine learning strives to make a system capable of autonomously achieving a level of `understanding' of provided information. Classification is one area in which machine learning is involved. The basic problem of classification is how a novel observation ought to be labelled. Despite machine learning algorithms being capable of providing such a label, based on previous data, typical algorithms do not provide explanations for a classification. Neither does an algorithm tell how `significant' a classification is: Should a decision maker consider this classification and act on it? The field of argumentation can be used to yield understandable reasons for a classification. PADUA is one approach that shows how rule mining can be combined with dialogues to reason about novel observations (Wardeh et al., 2009). Bench-Capon (2003) proposed value-based argumentation frameworks that accommodate the notion that certain arguments are stronger than others. The AGKA (Argumentative Grounded Knowledge Acquisition) architecture presented in this paper uses a decision tree, a machine learning algorithm, to learn from data. The decision tree is integrated into argumentative dialogues, similar to PADUA, to provide reasons for a classification. To rank the provided reasons by strength, expected utility is incorporated. The architecture is evaluated in a fraud detection scenario. Results indicate that its performance is comparable to other machine learning algorithms. AGKA is also effective in finding back the rules present in the data, but only if there is a clear binary distinction between classes. This research provides insights into the connections between machine learning (finding patterns in data), argumentation (providing reasons for and against hypotheses) and decision theory (finding the best course of action in a situation).

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:31
Last Modified: 15 Feb 2018 08:31

Actions (login required)

View Item View Item