Effective Data Mining Using Neural Networks

By Hongjun Lu, Rudy Setiono, Huan Liu. 1996

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Citations in the corpus

Rule Extraction Algorithm for Deep Neural Networks: A Review (2016, methodology): “Hruschka and Ebecken [30] have proposed an algorithm which is based on an algorithm called RX proposed by Lu, Setiono, and Liu [31].”
Rule extraction from trained adaptive neural networks using artificial immune systems (2009, background): “: +90 332 2233331; fax: +90 332 2412179.”
COMPARATIVE STUDY OF ATTRIBUTE SELECTION USING GAIN RATIO AND CORRELATION BASED FEATURE SELECTION (2010, background): “Advantages of neural networks, however, include their high tolerance to noisy data as their ability to classify patterns on which they have not been trained [12-15].”
A data mining approach for diagnosis of coronary artery disease (2013, background): “They can be used to model complex elationships between inputs and outputs or to find patterns n data [13].”
Learning Causal State Representations of Partially Observable Environments (2019, methodology): “The idea of extracting the implicit knowledge of a neural network [37, 11, 28, 15] is not novel and is rooted in early attempts to merge traditional rule-based methods with machine learning.”
Computational intelligence methods for rule-based data understanding (2004, methodology): “Local methods for extraction of conjunctive rules were proposed by Fu [68]–[71] and by Setiono and Liu [57]–[59], [72].”
Extracting rules from multilayer perceptrons in classification problems: A clustering-based approach (2006, background): “[41] and on an algorithm that eliminates NN redundant connections.”