Knowledge and information systems, 2010-07, Vol.24 (1), p.35-57
2010
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- Autor(en) / Beteiligte
- Titel
- A non-parametric semi-supervised discretization method
- Ist Teil von
- Knowledge and information systems, 2010-07, Vol.24 (1), p.35-57
- Ort / Verlag
- London: Springer-Verlag
- Erscheinungsjahr
- 2010
- Quelle
- Alma/SFX Local Collection
- Beschreibungen/Notizen
- Semi-supervised classification methods aim to exploit labeled and unlabeled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method, which adopts very low informative prior on data. This method discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes. Then, an in-depth comparison of this semi-supervised method with the original supervised MODL approach is presented. We demonstrate that the semi-supervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimization of the intervals bounds location.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0219-1377eISSN: 0219-3116DOI: 10.1007/s10115-009-0230-2Titel-ID: cdi_proquest_miscellaneous_753652741
- Format
- –
- Schlagworte
- Applied sciences, Asymptotic methods, Asymptotic properties, Bayesian analysis, Classification, Computer Science, Computer science, control theory, systems, Data mining, Data Mining and Knowledge Discovery, Data processing. List processing. Character string processing, Database Management, Discretization, Equivalence, Exact sciences and technology, Hypotheses, Information Storage and Retrieval, Information systems, Information Systems and Communication Service, Information Systems Applications (incl.Internet), Information systems. Data bases, Intervals, IT in Business, Mathematical models, Memory organisation. Data processing, Methods, Optimization, Probability, Regular Paper, Software, Studies, Trains