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Novel Approaches to Optimised Self-configuration in High
Performance Multiple Experts
M.C. Fairhurst and S. HoqueUniversity of Kent
UK
A. F. R. Rahman BCL Technologies Inc.
USA
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Basic Problem Statement
• Given a number of experts working on the same problem, is group decision superior to individual decisions?
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Is Democracy the answer?
• Infinite Number of Experts• Each Expert Should be Competent
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How Does It Relate to Character Recognition?
Each Expert has its:• Strengths and Weaknesses• Peculiarities• Fresh Approach to Feature Extraction• Fresh Approach to Classification• But NOT 100% Correct!
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Practical Resource Constraints
Unfortunately, We Have Limited• Number of Experts• Number of Training Samples• Feature Size• Classification Time• Memory Size
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Solution
• Clever Algorithms to Exploit Experts– Complimentary Information– Redundancy: Check and Balance– Simultaneous Use of Arbitrary Features and
Classification Routines
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How are they Employed?
Expert1 Expert 2 Expert n
Horizontal Systems
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How are they Employed?
Vertical Systems
Expert 1
Expert 2
Expert n
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How are they Employed?
• Combined System:– A hybrid of Horizontal and
VBertical– More Complicated to
Analyse?– Even more Complicated to
Optimise?
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What to Optimise?
• Number of Experts in a configuration• Type of Expert in each Position in the
hierarchy• Optimising Criteria
– Do we want a fast system? Or– Do we want an accurate System?
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Proposed Methodology
• Genetic Algorithm: A Generalised Search and Optimisation Method
• Problem Coding:– Chromosome Structure– Fitness Function– Genetic Operators
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Methodology • Chromosome Structure: A
Classifier is a Machine Obeying a Set of Production Rules. A Generalised Rule is:<classifier>::=<condition>:<message>– <condition> part is a pattern matching
device– <message> part is a feedback
mechanism
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Methodology
• Fitness Function: Fitness = Correct_Patterns/Total_Patterns
• Correct_Patterns corresponds to the number of correctly identified patterns in one cycle
• Total_Patterns corresponds to the number of total patterns being fed to the optimising process
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Methodology • Genetic Operators:
– Reproduction: • Weighted Roulette Wheel (Goldberg)• Stochastic Remainder Selection (Booker)• Tournament Selection (Brindle)
– Crossover: Swapping at [1,l-1]– Mutation: Random variation
• Single gene only
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Selection of a Specific Problem
Expert 1
Expert 2 Expert 3
Expert 4
Decision Compilation
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Selection of a Database• Machine Printed Characters Extracted from
British Envelopes• Collected Off-line• Total 34 Classes (0-9, A-Z, no Distinction
between 0/O and I/1)• Total Samples of Over 10,200 characters• Size Normalised to 16X24
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Performance of the Classifiers
Classifiers % Error
BWS 1.76
FWS 1.52
MPC 3.90
MLP 1.66
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Performance of the Combination
Classifier Position % Error
BWS 1
FWS 4
MPC 3
MLP 2
1.03
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The Optimised Combination
Classifier Position % Error
BWS Unused
FWS 2
MPC 1
MLP 3
0.92
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Generality of the Solution: Generation of a Vertical System
Expert 1
Expert 2
Expert 4
Decision Compilation
Input Pattern
Classification Decision
Expert 1
Expert 2
Expert 3
Input Pattern
Classification Decision
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Optimization for the Vertical System
Optimized Parameters
BWS Sub-set size
FWS Sub-set size
MPC Sub-set size
MLP Sub-set size
2 10 4 8 1 5 3 2
Combined % Error: 1.01
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Generality of the Solution: Generation of a Horizontal System
Expert 2 Expert 3
Expert 4
Decision Compilation
Input Pattern
Classification Decision
Expert 1 Expert 2
Expert 3
Decision Compilation
Input pattern
Decision Combination
Classification Decision
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Optimization for the Horizontal System
Optimized Parameter
BWS FWS MPC MLP Error %
Weighting Factor
0.14 0.53 0.11 0.22 0.92
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Conclusion• Multiple Expert Solutions can be made more
Robust by optimising these structures• Optimisation is made with GA approach• The adopted multiple expert configuration is
generic: it can produce both vertical and horizontal systems (in addition to the hybrid system)
• The optimization approach is generic: it man optimize both vertical and horizontal systems (in addition to the hybrid system)