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- W1899799171 abstract "A common problem in KDD (Knowledge Discovery in Databases) is the presence of noise in the data being mined. Neural networks are robust and have a good tolerance to noise, which makes them suitable for mining very noisy data. However, they have the well-known disadvantage of not discovering any high-level rule that can be used as a support for human decision making. In this work we present a method for extracting accurate, comprehensible rules from neural networks. The proposed method uses a genetic algorithm to find a good neural network topology. This topology is then passed to a rule extraction algorithm, and the quality of the extracted rules is then fed back to the genetic algorithm. The proposed system is evaluated on three public-domain data sets and the results show that the approach is valid." @default.
- W1899799171 created "2016-06-24" @default.
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- W1899799171 date "2002-11-08" @default.
- W1899799171 modified "2023-09-27" @default.
- W1899799171 title "Extracting comprehensible rules from neural networks via genetic algorithms" @default.
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- W1899799171 doi "https://doi.org/10.1109/ecnn.2000.886228" @default.
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