A process, based on argumentation theory, is described for classifying very noisy data. More specifically a process founded on a concept called "arguing from experience" is described where by several software agents "argue" about the classification of a new example given individual "case bases" containing previously classified examples. Two "arguing from experience" protocols are described: PADUA which has been applied to binary classification problems and PISA which has been applied to multi-class problems. Evaluation of both PADUA and PISA indicates that they operate with equal effectiveness to other classification systems in the absence of noise. However, the systems out-perform comparable systems given very noisy data. Keywords: Classification, Argumentation, Noisy data. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Wardeh, M., Coenen, F., & Bench-Capon, T. (2009). Arguing from experience to classifying noisy data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5691 LNCS, pp. 354–365). https://doi.org/10.1007/978-3-642-03730-6_28
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