Argument based machine learning

Možina, Martin; Žabkar, Jure; Bratko, Ivan

doi:10.1016/j.artint.2007.04.007

Cited by 102 publications

(57 citation statements)

References 8 publications

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“…On the contrary to the framework presented in this paper which makes a real bridge between argumentation and machine learning, in [24,34] Bratko and al consider argumentation as a tool for improving machine learning algorithms. In their approach, arguments are viewed as a bias for the hypotheses search through the hypothesis space.…”

Section: Resultsmentioning

confidence: 87%

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Agents that argue and explain classifications

Amgoud

Serrurier

2007

Auton Agent Multi-Agent Syst

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Argumentation is a promising approach used by autonomous agents for reasoning about inconsistent/incomplete/uncertain knowledge, based on the construction and the comparison of arguments. In this paper, we apply this approach to the classification problem, whose purpose is to construct from a set of training examples a model that assigns a class to any new example.We propose a formal argumentation-based model that constructs arguments in favor of each possible classification of an example, evaluates them, and determines among the conflicting arguments the acceptable ones. Finally, a "valid" classification of the example is suggested. Thus, not only the class of the example is given, but also the reasons behind that classification are provided to the user as well in a form that is easy to grasp. We show that such an argumentation-based approach for classification offers other advantages, like for instance classifying examples even when the set of training examples is inconsistent, and considering more general preference relations between hypotheses. In the particular case of concept learning, the results of version space theory developed by Mitchell are retrieved in an elegant way in our argumentation framework. Finally, we show that the model satisfies the rationality postulates identified in argumentation literature. This ensures that the model delivers sound results 1 .

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Section: Resultsmentioning

confidence: 87%

“…Recently, some researchers have tried to use argumentation techniques in machine learning [24,34]. On the contrary to the framework presented in this paper which makes a real bridge between argumentation and machine learning, in [24,34] Bratko and al consider argumentation as a tool for improving machine learning algorithms.…”

Section: Resultsmentioning

confidence: 89%

Agents that argue and explain classifications

Amgoud

Serrurier

2007

Auton Agent Multi-Agent Syst

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“…We are also planning, for some specific base classifiers, to study and evaluate methods to re-use the information provided by the oracle in order to re-learn the interactive classifier and improve its performance. If this information would include not only the labels but also some arguments supporting the decisions (Mozina et al, 2007) then the quality of the re-learned model could be even greater. Table 12 Macro-average values of the performance measures F 0:5 and EP 0:5 across the 31 databases, for each algorithm, using the random strategy.…”

Section: Discussionmentioning

confidence: 99%

Evaluation methods and strategies for the interactive use of classifiers

Acid

Campos

Hernández-Fernández

2012

International Journal of Human-Computer Studies

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We consider the scenario in which an automatic classifier (previously built) is available. It is used to classify new instances but, in some cases, the classifier may request the intervention of a human (the oracle), who gives it the correct class. In this scenario, first it is necessary to study how the performance of the system should be evaluated, as it cannot be based solely on the predictive accuracy obtained by the classifier but it should also take into account the cost of the human intervention; second, studying the concrete circumstances under which the classifier decides to query the oracle is also important. In this paper we study these two questions and include also an experimental evaluation of the different proposed alternatives.

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“…One Machine Learning approach which engages with issues related to comprehensibility is ArgumentBased Machine Learning (ABML) [9]. ABML applies methods from argumentation in combination with a rule-learning approach.…”

Section: Comprehensibilitymentioning

confidence: 99%

How Does Predicate Invention Affect Human Comprehensibility?

Schmid

Zeller

Besold

et al. 2017

Inductive Logic Programming

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract. During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as that of Mitchell, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present the results of experiments testing human comprehensibility of logic programs learned with and without predicate invention. Results indicate that comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. Permanent repository link

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Argument based machine learning

Cited by 102 publications

References 8 publications

Agents that argue and explain classifications

Agents that argue and explain classifications

Evaluation methods and strategies for the interactive use of classifiers

How Does Predicate Invention Affect Human Comprehensibility?

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