inductive learning in design: a method and case study concerning design of antifriction bearing...

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Inductive Learning in Design: A Method and Case Study Concerning Design of Antifriction Bearing Systems Machine Learning and Data Mining : Methods and Applications 1999 년 6 년 19 년 년년년 99406-810 년년년년년 년 년 년

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Inductive Learning in Design:A Method and Case Study Concerning Design of A

ntifriction Bearing Systems

Machine Learning and Data Mining : Methods and Applications

1999 년 6 월 19 일 토요일99406-810 산업공학과

허 원 창

Contents

Introduction Exemplary problem Testing and Training events Exemplary rule set obtained Empirical errors of learned rule set Degree of Confidence Conclusion

Introduction

A Method for Learning Design Rule– in design process - design knowledge is important but

ambiguous, and there are many solutions in design problem

– in applying Inductive Learning Method - recognizing design knowledge and representing it in the for of rule is important

– in this chapter - learning rules for selecting anti-friction bearing systems

Global Steps– defines attributes used for characterizing design examples

– describe design examples with selected attributes– determining training and testing examples– learning through AQ15c and obtaining rule set

Example Problem

Design of Bearing arrangement

Design Process

Training and Testing Events

Design Knowledge Source– catalogues of rolling bearing, text books on machine design,

special publications issued by producers of bearing.....– Conversions of quantitative data to qualitative data

Database Examples– bearing types : deep grove ball bearing, angular contact ball

bearing, self-aligning ball bearing, cylindrical roller bearings..– 10-26 events for each bearings– 101088 possible events– need more events from design experts

Domains of Attributes

Domains of Attributes

Exemplary Training Events

training events of the class ‘deep groove ball bearing’

Exemplary rules

exemplary rule concerning ‘deep groove ball bearing’

# of unique events that support rule

total # of events that support rule

Empirical Error of learned rule sets

overall empirical error rate

Empirical omission error rate

Empirical comission error rate

eventstestingofnumber

errorsofnumberEov

n

k

kom

komom kclassforexamplespositiveofnumber

kclassforerrorsomissionofnumberEE

nE

1

,1

n

k

kcm

kcmcm kclassforexamplesnegativeofnumber

kclassforerrorscomissionofnumberEE

nE

1

,1

Testing Results

Testing results using ‘leave-one-out’ method

Evaluation of Training Example

Evaluation of training example

Exemplary Degree of Confidence

exemplary Degree of confidence

Conclusion

In problems of deriving useful design knowledge in order to aid designer in routine design task– The feasibility of the application of machine learning in case

of selecting the type of bearing.– can suggests several solution to designers.– The ruleset obtained features high degree of accuracy.– Further verification of results require cooperation with skilled

designers