Symbolic Rule Extraction and Visualization using Network Function Inversion

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03096691" target="_blank" >RIV/68407700:21230/04:03096691 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Symbolic Rule Extraction and Visualization using Network Function Inversion
Original language description
An important type of knowledge representation is sets of symbolic IF.THEN rules. Symbolic rules are well understood by humans and amenable to symbolic manipulation and inference techniques. Rule extraction using neural networks presents an attractive approach to knowledge acquisition because it combines the straightforward manner in which neural networks can learn from training data with the above given advantages of rule sets. Rule extraction using neural networks proceeds by first training a neural network on the analyzed data, followed by transformation of the resulting network into a corresponding rule set representation. The article presents a rule induction technique that employ inversion of the network function in rule induction process. The technique is then generalized to a three-stage rule induction method and its advantages are discussed, including the use of decision region connectivity analysis for higher level-description of auxiliary middle model.
Czech name
Extrakce symbolických pravidel inverzí síťové funkce
Czech description
Článek popisuje metodu extrakce pravidel z dat založenou prvotně na naučení neuronové sítě a následném inverzi realizované síťové funkce a její aproximace pomocí IF-THEN pravidel.

Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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