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Encouraging Complementary Fuzzy Rules within Iterative Rule Learning

Encouraging Complementary Fuzzy Rules within Iterative Rule Learning. Motivation. Gain deeper understanding of IRL strategy for fuzzy rule base induction Test ACO as rule discovery mechanism within IRL. Training Set. adjustments. adjustments. best rule. Rule Base. Rule 1. best rule.

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Encouraging Complementary Fuzzy Rules within Iterative Rule Learning

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  1. Encouraging Complementary Fuzzy Rules within Iterative Rule Learning

  2. Motivation • Gain deeper understanding of IRL strategy for fuzzy rule base induction • Test ACO as rule discovery mechanism within IRL

  3. Training Set adjustments adjustments best rule Rule Base Rule 1 best rule Rule 2 . . Rule k best rule SPBAk IRL – Iterative Rule Learning SPBA1 SPBA2 . . .

  4. Ant Colony Optimisation – The Basics • Problem representation • Probabilistic transition rule • Local heuristic • Constraint satisfaction method • Fitness function • Pheromone updating strategy Constructionist, iterative algorithm:

  5. Iteration 1 Iteration m Iteration 2 Rule Rule Rule 1 . 1 m 2 . . 1 1 Rule Rule Rule Rule Rule Rule 1 . 2 1 . n m 2 . . 2 2 m 2 . . n n best rule itn. 2 best rule itn.1 best rule itn. m Rule 1.2 Rule 2.5 . . Rule m.3 Rule base Rule 1 best rule ACO for Fuzzy Rule Induction ACO 1 . . . . . . . . . .

  6. FRANTIC Rule Construction… OUTLOOK HUMIDITY Cloudy Rain Not _ H Humid Sunny Cool Hot Mild TEMPERATURE Not _ W Wind WIND

  7. FRANTIC Rule Construction… OUTLOOK HUMIDITY Cloudy Rain Not _ H Humid Sunny CHECK: minCasesPerRule Cool Hot Mild TEMPERATURE Not _ W Wind WIND

  8. y n n u S FRANTIC Rule Construction… OUTLOOK HUMIDITY X Cloudy X Rain Not _ H Humid Cool Hot Mild CHECK! TEMPERATURE Not _ W Wind WIND

  9. FRANTIC Rule Construction… OUTLOOK HUMIDITY X Cloudy X Rain Not _ H Humid CHECK! Cool Hot S u n n y Mild X TEMPERATURE Not _ W Wind WIND

  10. IRL – Training Set Adjustment • Removal of training examples • Re-weighting of training examples based on current best rule (class-independent IRL, Hoffmann 2004) • Use of indicators for cooperation/competition between current rule and rules already in rule base (class-dependent IRL, Gonzales & Perez 1999)

  11. Classification Accuracy…

  12. Number of Rules…

  13. minCasesPerRule Robustness… Saturday Morning dataset – predictive accuracy while varying parameter

  14. minCasesPerRule Robustness… Iris dataset – predictive accuracy while varying parameter

  15. Future Work • Identify and analyse parameter interactions • Investigate impact of training adjustment method on parameter robustness • Devise, explore and compare alternative approaches to training set adjustment • Deepen understanding of IRL strategy by comparing different rule discovery mechanisms

  16. Encouraging Complementary Fuzzy Rules within Iterative Rule Learning

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