?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Empirical+learning+aided+by+weak+domain+knowledge+in+the+form+of+feature+importance&rft.creator=Iqbal%2C+Ridwan+Al&rft.subject=Machine+Learning&rft.description=Standard+hybrid+learners+that+use+domain+knowledge+require+stronger+knowledge+that+is+hard+and+expensive+to+acquire.+However%2C+weaker+domain+knowledge+can+benefit+from+prior+knowledge+while+being+cost+effective.+Weak+knowledge+in+the+form+of+feature+relative+importance+(FRI)+is+presented+and+explained.+Feature+relative+importance+is+a+real+valued+approximation+of+a+feature%E2%80%99s+importance+provided+by+experts.+Advantage+of+using+this+knowledge+is+demonstrated+by+IANN%2C+a+modified+multilayer+neural+network+algorithm.+IANN+is+a+very+simple+modification+of+standard+neural+network+algorithm+but+attains+significant+performance+gains.+Experimental+results+in+the+field+of+molecular+biology+show+higher+performance+over+other+empirical+learning+algorithms+including+standard+backpropagation+and+support+vector+machines.+IANN+performance+is+even+comparable+to+a+theory+refinement+system+KBANN+that+uses+stronger+domain+knowledge.+This+shows+Feature+relative+importance+can+improve+performance+of+existing+empirical+learning+algorithms+significantly+with+minimal+effort.&rft.date=2010-06-04&rft.type=Preprint&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F6855%2F1%2Ffri.pdf&rft.identifier=++Iqbal%2C+Ridwan+Al++(2010)+Empirical+learning+aided+by+weak+domain+knowledge+in+the+form+of+feature+importance.++%5BPreprint%5D++++(Unpublished)++&rft.relation=http%3A%2F%2Fcogprints.org%2F6855%2F