Arguing from Experience to Classifying Noisy Data

Wardeh, Maya; Coenen, Frans; Bench‐Capon, Trevor

doi:10.1007/978-3-642-03730-6_28

Cited by 2 publications

(1 citation statement)

References 17 publications

(26 reference statements)

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“…PISA was first evaluated in Wardeh et al (2009a), in which it was shown that PISA also performed better than, or as well as, its competitors. The particular benefits of Arguing from experience for classification were, however, shown most clearly in Wardeh, Bench-Capon, and Coenen (2009b), where PISA was shown to produce robust results even when the data sets of the included agents were infected with different levels of noise (up to 50%).…”

Section: Experimental Evaluationmentioning

confidence: 99%

Arguing from experience using multiple groups of agents

Wardeh

Bench‐Capon

Coenen

2011

Argument & Computation

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A framework to support "Arguing from Experience" using groups of collaborating agents (termed participant agents/players) is described. The framework is an extension of the PISA multi-party arguing from experience framework. The original version of PISA allowed n participants to promote n goals (one each) for a given example. The described extension of PISA allows individuals with the same goals to pool their resources by forming "groups". The framework is fully described and its effectiveness illustrated using a number of classification scenarios. The main finding is that by using groups more accurate results can be obtained than when agents operate in isolation.

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Section: Experimental Evaluationmentioning

confidence: 99%

Arguing from experience using multiple groups of agents

Wardeh

Bench‐Capon

Coenen

2011

Argument & Computation

Self Cite

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Classification in the Presence of Label Noise: A Survey

Frénay‬

Verleysen

2014

IEEE Trans. Neural Netw. Learning Syst.

1,423

917

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Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

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Arguing from Experience to Classifying Noisy Data

Cited by 2 publications

References 17 publications

Arguing from experience using multiple groups of agents

Arguing from experience using multiple groups of agents

Classification in the Presence of Label Noise: A Survey

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