Novel Approaches to Optimised Self-configuration in High Performance Multiple Experts M.C. Fairhurst and S. Hoque University of Kent UK A.F. R. Rahman.

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Presentation transcript:

Novel Approaches to Optimised Self-configuration in High Performance Multiple Experts M.C. Fairhurst and S. Hoque University of Kent UK A.F. R. Rahman BCL Technologies Inc. USA

Basic Problem Statement Given a number of experts working on the same problem, is group decision superior to individual decisions?

Is Democracy the answer? Infinite Number of Experts Each Expert Should be Competent

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!

Practical Resource Constraints Unfortunately, We Have Limited Number of Experts Number of Training Samples Feature Size Classification Time Memory Size

Solution Clever Algorithms to Exploit Experts –Complimentary Information –Redundancy: Check and Balance –Simultaneous Use of Arbitrary Features and Classification Routines

How are they Employed? Horizontal Systems

How are they Employed? Vertical Systems

How are they Employed? Combined System: –A hybrid of Horizontal and VBertical –More Complicated to Analyse? –Even more Complicated to Optimise?

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?

Proposed Methodology Genetic Algorithm: A Generalised Search and Optimisation Method Problem Coding: –Chromosome Structure –Fitness Function –Genetic Operators

Methodology Chromosome Structure: A Classifier is a Machine Obeying a Set of Production Rules. A Generalised Rule is: ::= : – part is a pattern matching device – part is a feedback mechanism

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

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

Selection of a Specific Problem

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

Performance of the Classifiers

Performance of the Combination

The Optimised Combination

Generality of the Solution: Generation of a Vertical System

Optimization for the Vertical System Optimized Parameters BWSSub-set size FWSSub-set size MPCSub-set size MLPSub-set size Combined % Error: 1.01

Generality of the Solution: Generation of a Horizontal System

Optimization for the Horizontal System Optimized Parameter BWSFWSMPCMLPError % Weighting Factor

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)