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Control and Cybernetics

Tytuł artykułu

A hybrid approach to dimension reduction in classification

Autorzy Krawczak, M.  Szkatuła, G. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN 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.
Słowa kluczowe
EN data series   dimension reduction   envelopes   essential attributes   heteroassociation   machine learning from examples   decision rules   classification  
Wydawca Systems Research Institute, Polish Academy of Sciences
Czasopismo Control and Cybernetics
Rocznik 2011
Tom Vol. 40, no 2
Strony 527--551
Opis fizyczny Bibliogr. 28 poz.
autor Krawczak, M.
autor Szkatuła, G.
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