ISSN:
1573-773X
Keywords:
backpropagation
;
feature selection
;
logical rule extraction
;
MLP
;
neural networks
;
probability density estimation
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract Three neural-based methods for extraction of logical rules from data are presented. These methods facilitate conversion of graded response neural networks into networks performing logical functions. MLP2LN method tries to convert a standard MLP into a network performing logical operations (LN). C-MLP2LN is a constructive algorithm creating such MLP networks. Logical interpretation is assured by adding constraints to the cost function, forcing the weights to ±1 or 0. Skeletal networks emerge ensuring that a minimal number of logical rules are found. In both methods rules covering many training examples are generated before more specific rules covering exceptions. The third method, FSM2LN, is based on the probability density estimation. Several examples of performance of these methods are presented.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1009670302979
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