Argumentation based joint learning: a novel ensemble learning approach.

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2015
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Abstract
Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.
Reference Key
xu2015argumentationplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xu, Junyi;Yao, Li;Li, Le;
Journal PloS one
Year 2015
DOI
10.1371/journal.pone.0127281
URL
Keywords
multi-agent systems information foraging medical data management medlineplus page ranking swarm intelligence web intelligence ant colony optimization (aco) tabu search

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