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Encouraging Complementary Fuzzy Rules within Iterative Rule Learning Michelle Galea School of Informatics University of Edinburgh Edinburgh, UK Qiang Shen.

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Presentation on theme: "Encouraging Complementary Fuzzy Rules within Iterative Rule Learning Michelle Galea School of Informatics University of Edinburgh Edinburgh, UK Qiang Shen."— Presentation transcript:

1 Encouraging Complementary Fuzzy Rules within Iterative Rule Learning Michelle Galea School of Informatics University of Edinburgh Edinburgh, UK Qiang Shen Department of Computer Science University of Wales Aberystwyth, UK Vishal Singh Larson & Toubro, EmSys Ltd. Bangalore, India

2 Motivation 1. Gain deeper understanding of IRL strategy for fuzzy rule base induction 2. Test ACO as rule discovery mechanism within IRL

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

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

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

6 FRANTIC Rule Construction… TEMPERATURE WIND Wind Not_W Hot Cool Mild OUTLOOK Sunny Cloudy Rain HUMIDITY Humid Not_H

7 FRANTIC Rule Construction… TEMPERATURE WIND Wind Not_W Hot Cool Mild OUTLOOK Sunny Cloudy Rain HUMIDITY Humid Not_H CHECK: minCasesPerRule

8 FRANTIC Rule Construction… TEMPERATURE WIND Wind Not_W Hot Cool Mild OUTLOOK Cloudy Rain HUMIDITY Humid Not_H CHECK! X X S u n n y

9 FRANTIC Rule Construction… TEMPERATURE WIND Wind Not_W Hot Cool Mild OUTLOOK Cloudy Rain HUMIDITY Humid Not_H CHECK! X X S u n n y X

10 IRL – Training Set Adjustment Removal of training examples Removal of training examples Re-weighting of training examples based on current best rule (class-independent IRL, Hoffmann 2004) 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) 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 Identify and analyse parameter interactions Investigate impact of training adjustment method on parameter robustness Investigate impact of training adjustment method on parameter robustness Devise, explore and compare alternative approaches to training set adjustment Devise, explore and compare alternative approaches to training set adjustment Deepen understanding of IRL strategy by comparing different rule discovery mechanisms Deepen understanding of IRL strategy by comparing different rule discovery mechanisms

16 Encouraging Complementary Fuzzy Rules within Iterative Rule Learning Michelle Galea School of Informatics University of Edinburgh Edinburgh, UK Qiang Shen Department of Computer Science University of Wales Aberystwyth, UK Vishal Singh Larson & Toubro, EmSys Ltd. Bangalore, India


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