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Wyświetlanie 1-2 z 2
Tytuł:
Heterogeneous distance functions for prototype rules : influence of parameters on probability estimation
Autorzy:
Blachnik, M.
Duch, W.
Wieczorek, T.
Powiązania:
https://bibliotekanauki.pl/articles/92882.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
prototype rules
probability estimation
heterogeneous distance functions
similarity-based methods
classification
data mining
Opis:
An interesting and little explored way to understand data is based on prototype rules (P-rules). The goal of this approach is to find optimal similarity (or distance) functions and position of prototypes to which unknown vectors are compared. In real applications similarity functions frequently involve different types of attributes, such as continuous, discrete, binary or nominal. Heterogeneous distance functions that may handle such diverse information are usually based on probability distance measure, such as the Value Difference Metrics (VDM). For continuous attributes calculation of probabilities requires estimations of probability density functions. This process requires careful selection of several parameters that may have important impact on the overall classification of accuracy. In this paper, various heterogeneous distance function based on VDM measure are presented, among them some new heterogeneous distance functions based on different types of probability estimation. Results of many numerical experiments with such distance functions are presented on artificial and real datasets, and quite simple P-rules for several heterogeneous databases extracted.
Źródło:
Studia Informatica : systems and information technology; 2006, 1(7); 19-30
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural methods of knowledge extraction
Autorzy:
Duch, W.
Adamczak, R.
Grąbczewski, K.
Jankowski, N.
Powiązania:
https://bibliotekanauki.pl/articles/206250.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
diagnostyka medyczna
optymalizacja
reguła logiczna
reguła rozmyta
wspomaganie decyzji
data mining
decision support
fuzzy rules
logical rules
medical diagnosis
optimization
Opis:
Contrary to the common opinion, neural networks may be used for knowledge extraction. Recently, a new methodology of logical rule extraction, optimization and application of rule-based systems has been described. C-MLP2LN algorithm, based on constrained multilayer perceptron network, is described here in details and the dynamics of a transition from neural to logical system illustrated. The algorithm handles real-valued features, determining appropriate linguistic variables or membership functions as a part of the rule extraction process. Initial rules are optimized by exploring the accuracy/simplicity tradeoff at the rule extraction stage and the one between reliability of rules and rejection rate at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Comments are made on application of neural networks to knowledge discovery in the benchmark and real life problems.
Źródło:
Control and Cybernetics; 2000, 29, 4; 997-1017
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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