Traditional and robust vector selection methods for use with similarity based models
- Nuclear Engineering Dept., Univ. of Tennessee, Knoxville, TN 37996-2300 (United States)
Vector selection, or instance selection as it is often called in the data mining literature, performs a critical task in the development of nonparametric, similarity based models. Nonparametric, similarity based modeling (SBM) is a form of 'lazy learning' which constructs a local model 'on the fly' by comparing a query vector to historical, training vectors. For large training sets the creation of local models may become cumbersome, since each training vector must be compared to the query vector. To alleviate this computational burden, varying forms of training vector sampling may be employed with the goal of selecting a subset of the training data such that the samples are representative of the underlying process. This paper describes one such SBM, namely auto-associative kernel regression (AAKR), and presents five traditional vector selection methods and one robust vector selection method that may be used to select prototype vectors from a larger data set in model training. The five traditional vector selection methods considered are min-max, vector ordering, combination min-max and vector ordering, fuzzy c-means clustering, and Adeli-Hung clustering. Each method is described in detail and compared using artificially generated data and data collected from the steam system of an operating nuclear power plant. (authors)
- Research Organization:
- American Nuclear Society, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
- OSTI ID:
- 22030046
- Resource Relation:
- Conference: NPIC and HMIT 2006: 5. International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology, Albuquerque, NM (United States), 12-16 Nov 2006; Other Information: Country of input: France; 31 refs.; Related Information: In: Proceedings of the 5. International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology| 1430 p.
- Country of Publication:
- United States
- Language:
- English
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