RULEX Algorithm

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A RULEX Algorithm is a Rule Induction Algorithm that extracts if-then rules from a trained CEBP Network.



References

2005

Predicate rules using Accuracy Training (%) Accuracy Testing (%) Fidelity to the network (%)
LAP PCC 98.28 95.05 99.04
PBT 98.21 95.15 98.88
RuleVI PCC 97.65 89.57 98.27
PBT 97.59 84.71 96.87
Rulex CEBPN 96.41 89.51 93.23
C4.5 96.99 94.05
Foil 97.1 83.98
Table 1. The relative average predictive accuracy of predicate rules over 10 data sets.

2003

IF [math]\quad \displaystyle \forall \;1 \leq i \leq n : x_i \in \left[ x_{i\;lower} , x_{i\;upper}\right][/math]
THEN pattern belongs to the target class
from the hyper-ellipsoid basis functions of the restricted local cluster network (...)

Table 3.5 below gives an outline of the RULEX algorithm (...)


rulex() {
   create_data_structures();
create_domain_description();
for each local cluster
   for each ridge function
   calculate_ridge_limits();
while redundancies remain
remove_redundant_rules();
remove_redundant_antecedents();
merge_antecedents();
endwhile;
feed_forward_test_set();
display_rule_set();
} end rulex

Table 3.5 – The RULEX Algorithm.

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1996

1995


  1. Ross Hayward. Alan Tickle, and Joachim Diederich (1996). "Extracting rules for grammar recognition from Cascade-2 networks". In: Wermter S., Riloff E., Scheler G. (eds) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. IJCAI 1995. DOI:10.1007/3-540-60925-3_37
  2. R. Hayward, C. Ho-Stuart, and J. Diederich (1997). "Neural networks as oracles for rule extraction". In: Connectionist System for Knowledge Representation and Deduction
  3. Robert Andrews, and Shlomo Geva (1994). "Rule extraction from a constrained error back propagation MLP". In: Proc. of 5th Australian Conference on Neural Networks.