?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Using+Feature+Weights+to+Improve+Performance+of+Neural+Networks&rft.creator=Iqbal%2C+Ridwan+Al&rft.subject=Artificial+Intelligence&rft.subject=Machine+Learning&rft.subject=Neural+Nets&rft.description=Different+features+have+different+relevance+to+a+particular+learning+problem.+Some+features+are+less+relevant%3B+while+some+very+important.+Instead+of+selecting+the+most+relevant+features+using+feature+selection%2C+an+algorithm+can+be+given+this+knowledge+of+feature+importance+based+on+expert+opinion+or+prior+learning.+Learning+can+be+faster+and+more+accurate+if+learners+take+feature+importance+into+account.+Correlation+aided+Neural+Networks+(CANN)+is+presented+which+is+such+an+algorithm.+CANN+treats+feature+importance+as+the+correlation+coefficient+between+the+target+attribute+and+the+features.+CANN+modifies+normal+feed-forward+Neural+Network+to+fit+both+correlation+values+and+training+data.+Empirical+evaluation+shows+that+CANN+is+faster+and+more+accurate+than+applying+the+two+step+approach+of+feature+selection+and+then+using+normal+learning+algorithms.&rft.date=2011-01-25&rft.type=Preprint&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F7179%2F1%2FCANN.pdf&rft.identifier=++Iqbal%2C+Ridwan+Al++(2011)+Using+Feature+Weights+to+Improve+Performance+of+Neural+Networks.++%5BPreprint%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F7179%2F