Abstract
This paper proposes a novel Distributed Data Mining (DDM) approach based on the Agents and Artifacts paradigm, as implemented in CArtAgO [9], where artifacts encapsulate data mining tools, inherited from Weka, that agents can use while engaged in collaborative, distributed learning processes. Target hypothesis are currently constrained to decision trees built with J48, but the approach is flexible enough to allow different kinds of learning models. The twofold contribution of this work includes: i) JaCA-DDM: an extensible tool implemented in the agent oriented programming language Jason [2] and CArtAgO [10,9] to experiment DDM agent-based approaches on different, well known training sets. And ii) A collaborative protocol where an agent builds an initial decision tree, and then enhances this initial hypothesis using instances from other agents that are not covered yet (counter examples); reducing in this way the number of instances communicated, while preserving accuracy when compared to full centralized approaches.
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Limón, X., Guerra-Hernández, A., Cruz-Ramírez, N., Grimaldo, F. (2013). An Agents and Artifacts Approach to Distributed Data Mining. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_30
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DOI: https://doi.org/10.1007/978-3-642-45111-9_30
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