Iterative Project Management Module 1 - Iterative and Incremental Development.
Encouraging Complementary Fuzzy Rules within Iterative Rule Learning
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Encouraging Encouraging Complementary Complementary Fuzzy Rules within Fuzzy Rules within Iterative Rule Learning Iterative Rule Learning Michelle Galea Michelle Galea School of School of Informatics Informatics University of University of Edinburgh Edinburgh Edinburgh, UK Edinburgh, UK Qiang Shen Qiang Shen Department of Computer Department of Computer Science Science University of Wales University of Wales Aberystwyth, UK Aberystwyth, UK Vishal Singh Vishal Singh Larson & Toubro, EmSys Ltd. Larson & Toubro, EmSys Ltd. Bangalore, India Bangalore, India
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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. - PowerPoint PPT Presentation
Transcript of Encouraging Complementary Fuzzy Rules within Iterative Rule Learning
Encouraging Complementary Encouraging Complementary Fuzzy Rules within Fuzzy Rules within
Iterative Rule LearningIterative Rule Learning
Michelle GaleaMichelle GaleaSchool of InformaticsSchool of Informatics
University of EdinburghUniversity of EdinburghEdinburgh, UKEdinburgh, UK
Qiang ShenQiang ShenDepartment of Computer ScienceDepartment of Computer Science
University of WalesUniversity of WalesAberystwyth, UKAberystwyth, UK
Vishal SinghVishal SinghLarson & Toubro, EmSys Ltd.Larson & Toubro, EmSys Ltd.
Bangalore, IndiaBangalore, India
MotivationMotivation
1.1. Gain deeper understanding of IRL Gain deeper understanding of IRL strategy for fuzzy rule base inductionstrategy for fuzzy rule base induction
2.2. Test ACO as rule discovery mechanism Test ACO as rule discovery mechanism within IRLwithin IRL
IRL – Iterative Rule LearningIRL – Iterative Rule Learning
Training Set
Rule Base
SPBA1
adjustments
SPBA2
Rule 1
best rule
adjustments...
Rule 2best rule
SPBAk
.
. Rule k
best rule
Ant Colony Optimisation – Ant Colony Optimisation – The BasicsThe Basics
Problem representationProblem representation
Probabilistic transition ruleProbabilistic transition rule
Local heuristicLocal heuristic
Constraint satisfaction methodConstraint satisfaction method
Fitness functionFitness function
Pheromone updating strategyPheromone updating strategy
Constructionist, iterative algorithm:Constructionist, iterative algorithm:
ACO for Fuzzy Rule InductionACO for Fuzzy Rule InductionACO 1
Iteration 1
Rule 1.1
Rule 1.2
Rule 1.n
Rule 1.2
best rule itn.1
Iteration 2
Rule 2.1
Rule 2.2
Rule 2.n
Rule 2.5
best rule itn. 2
. . . . . . . . . .
..Rule m.3
best rule itn. m
Iteration m
Rule m.1
Rule m.2
Rule m.n
Rule baseRule 1best rule
FRANTICFRANTIC Rule Construction… Rule Construction…
TEMPERATURE
WIND
Wind
Not_W
Hot
Cool
Mild
OUTLOOK
Sunny
CloudyRain
HUMIDITY
HumidNot_H
FRANTICFRANTIC Rule Construction… Rule Construction…
TEMPERATURE
WIND
Wind
Not_W
Hot
Cool
Mild
OUTLOOK
Sunny
CloudyRain
HUMIDITY
HumidNot_H
CHECK: minCasesPerRule
FRANTICFRANTIC Rule Construction… Rule Construction…
TEMPERATURE
WIND
Wind
Not_W
Hot
Cool
Mild
OUTLOOK
CloudyRain
HUMIDITY
HumidNot_H
CHECK!
XX
Sunny
FRANTICFRANTIC Rule Construction… Rule Construction…
TEMPERATURE
WIND
Wind
Not_W
Hot
Cool
Mild
OUTLOOK
CloudyRain
HUMIDITY
HumidNot_H
CHECK!
XX
Sunny
X
IRL – Training Set AdjustmentIRL – Training Set Adjustment
Removal of training examplesRemoval of training examples
Re-weighting of training examples based on Re-weighting of training examples based on current best rule (class-independent IRL, current best rule (class-independent IRL, Hoffmann 2004)Hoffmann 2004)
Use of indicators for cooperation/competition Use of indicators for cooperation/competition between current rule and rules already in rule between current rule and rules already in rule base (class-dependent IRL, Gonzales & Perez base (class-dependent IRL, Gonzales & Perez 1999)1999)
Classification Accuracy…Classification Accuracy…
0102030405060708090
100
SM Image Iris WT
Dataset
% A
ccur
acy
Case Removal
Case Weighting
Number of Rules…Number of Rules…
0
5
10
15
20
25
30
35
40
SM Image Iris WT
Dataset
Num
ber o
f Rul
es
Case Removal
Case Weighting
minCasesPerRuleminCasesPerRule Robustness… Robustness…
value Case Removal
Case Weighting
5 37.50 56.254 25.00 56.252 47.50 59.38
Range 22.50 3.13
Saturday Morning dataset – predictive accuracy while varying parameter
minCasesPerRuleminCasesPerRule Robustness… Robustness…
Iris dataset – predictive accuracy while varying parameter
value Case Removal
Case Weighting
12 85.73 90.6710 92.27 93.008 89.93 92.675 85.13 93.80
Range 7.14 3.13
Future WorkFuture Work
Identify and analyse parameter interactions Identify and analyse parameter interactions
Investigate impact of training adjustment method Investigate impact of training adjustment method on parameter robustnesson parameter robustness
Devise, explore and compare alternative Devise, explore and compare alternative approaches to training set adjustmentapproaches to training set adjustment
Deepen understanding of IRL strategy by Deepen understanding of IRL strategy by comparing different rule discovery mechanismscomparing different rule discovery mechanisms
Encouraging Complementary Encouraging Complementary Fuzzy Rules within Fuzzy Rules within
Iterative Rule LearningIterative Rule Learning
Michelle GaleaMichelle GaleaSchool of InformaticsSchool of Informatics
University of EdinburghUniversity of EdinburghEdinburgh, UKEdinburgh, UK
Qiang ShenQiang ShenDepartment of Computer ScienceDepartment of Computer Science
University of WalesUniversity of WalesAberystwyth, UKAberystwyth, UK
Vishal SinghVishal SinghLarson & Toubro, EmSys Ltd.Larson & Toubro, EmSys Ltd.
Bangalore, IndiaBangalore, India
