Imperial College London

ProfessorMichaelLowe

Faculty of EngineeringDepartment of Mechanical Engineering

Head of Department of Mechanical Engineering
 
 
 
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Contact

 

+44 (0)20 7594 7000m.lowe Website

 
 
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Assistant

 

Ms Nina Hancock +44 (0)20 7594 7068

 
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Location

 

577DCity and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Shipway:2019:10.1007/s10921-019-0574-9,
author = {Shipway, NJ and Huthwaite, P and Lowe, MJS and Barden, TJ},
doi = {10.1007/s10921-019-0574-9},
journal = {Journal of Nondestructive Evaluation},
title = {Performance based modifications of random forest to perform automated defect detection for fluorescent penetrant inspection},
url = {http://dx.doi.org/10.1007/s10921-019-0574-9},
volume = {38},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The established Machine Learning algorithm Random Forest (RF) has previously been shown to be effective at performing automated defect detection for test pieces which have been processed using fluorescent penetrant inspection (FPI). The work presented here investigates three methods (two previously proposed in other fields, one novel method) of modifying the FPI RF based on the individual performance of decision trees within the RF. Evaluating based on the 2 Score, which is the harmonic mean of precision and recall which places a larger weighting on recall, it is possible to reduce the RF in size by up to 50%, improving speed and memory requirements, whilst still gain equivalent results to a full RF. Introducing a performance based weighting or retraining decision trees which fall below a certain performance level however, offers no improvement on results for the increased computation time required to implement.
AU - Shipway,NJ
AU - Huthwaite,P
AU - Lowe,MJS
AU - Barden,TJ
DO - 10.1007/s10921-019-0574-9
PY - 2019///
SN - 0195-9298
TI - Performance based modifications of random forest to perform automated defect detection for fluorescent penetrant inspection
T2 - Journal of Nondestructive Evaluation
UR - http://dx.doi.org/10.1007/s10921-019-0574-9
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000461387800002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/69659
VL - 38
ER -