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DAWAK
2009
Springer

Arguing from Experience to Classifying Noisy Data

14 years 4 months ago
Arguing from Experience to Classifying Noisy Data
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.
Maya Wardeh, Frans Coenen, Trevor J. M. Bench-Capo
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where DAWAK
Authors Maya Wardeh, Frans Coenen, Trevor J. M. Bench-Capon
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