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Titolo:
Interactive machine learning: letting users build classifiers
Autore:
Ware, M; Frank, E; Holmes, G; Hall, M; Witten, IH;
Indirizzi:
Univ Waikato, Dept Comp Sci, Hamilton, New Zealand Univ Waikato HamiltonNew Zealand Dept Comp Sci, Hamilton, New Zealand
Titolo Testata:
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
fascicolo: 3, volume: 55, anno: 2001,
pagine: 281 - 292
SICI:
1071-5819(200109)55:3<281:IMLLUB>2.0.ZU;2-2
Fonte:
ISI
Lingua:
ENG
Keywords:
interactive learning; classification; decision trees; visualization;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Social & Behavioral Sciences
Engineering, Computing & Technology
Citazioni:
10
Recensione:
Indirizzi per estratti:
Indirizzo: Ware, M Univ Waikato, Dept Comp Sci, Hamilton, New Zealand Univ Waikato Hamilton New Zealand mp Sci, Hamilton, New Zealand
Citazione:
M. Ware et al., "Interactive machine learning: letting users build classifiers", INT J HUM-C, 55(3), 2001, pp. 281-292

Abstract

According to standard procedure, building a classifier using machine learning is a fully automated process that follows the preparation of training data by a domain expert. In contrast, interactive machine learning engages users in actually generating the classifier themselves. This offers a natural way of integrating background knowledge into the modelling stage-as long as interactive tools can be designed that support efficient and effective communication. This paper shows that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms. It demonstrates that users-even users who are not domain experts-can often construct good classifiers, without any help from a learning algorithm, using a simple two-dimensional visual interface. Experiments on real data demonstrate that, not surprisingly, success hinges on the domain: if a few attributes can support good predictions, users generate accurate classifiers, whereas domains with many high-order attribute interactions favour standard machine learning techniques. We also present an artificial example where domain knowledge allows an "expert user" to create a much more accurate model than automatic learning algorithms. These results indicate that our system has the potential to produce highly accurate classifiers in the hands of a domain expert who has a strong interest in the domainand therefore some insights into how to partition the data. Moreover, small expert-defined models offer the additional advantage that they will generally be more intelligible than those generated by automatic techniques. (C)2001 Academic Press.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/02/20 alle ore 14:15:45