- Tytuł:
- A hybrid approach to dimension reduction in classification
- Autorzy:
-
Krawczak, M.
Szkatuła, G. - Powiązania:
- https://bibliotekanauki.pl/articles/206425.pdf
- Data publikacji:
- 2011
- Wydawca:
- Polska Akademia Nauk. Instytut Badań Systemowych PAN
- Tematy:
-
data series
dimension reduction
envelopes
essential attributes
heteroassociation
machine learning from examples
decision rules
classification - Opis:
- In this paper we introduce a hybrid approach to data series classification. The approach is based on the concept of aggregated upper and lower envelopes, and the principal components here called 'essential attributes', generated by multilayer neural networks. The essential attributes are represented by outputs of hidden layer neurons. Next, the real valued essential attributes are nominalized and symbolic data series representation is obtained. The symbolic representation is used to generate decision rules in the IF. . . THEN. . . form for data series classification. The approach reduces the dimension of data series. The efficiency of the approach was verified by considering numerical examples.
- Źródło:
-
Control and Cybernetics; 2011, 40, 2; 527-551
0324-8569 - Pojawia się w:
- Control and Cybernetics
- Dostawca treści:
- Biblioteka Nauki
Artykuł