Comput. Biol. Med. - Relabeling algorithm for retrieval of noisy instances and improving prediction quality.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ featur(3375) classif(2383) classifi(1994) }
{ howev(809) still(633) remain(590) }
{ algorithm(1844) comput(1787) effici(935) }
{ model(2656) set(1616) predict(1553) }
{ imag(1947) propos(1133) code(1026) }
{ medic(1828) order(1363) alert(1069) }
{ imag(1057) registr(996) error(939) }
{ framework(1458) process(801) describ(734) }
{ extract(1171) text(1153) clinic(932) }
{ search(2224) databas(1162) retriev(909) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ use(2086) technolog(871) perceiv(783) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
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{ method(1219) similar(1157) match(930) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
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{ intervent(3218) particip(2042) group(1664) }
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{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

A relabeling algorithm for retrieval of noisy instances with binary outcomes is presented. The relabeling algorithm iteratively retrieves, selects, and re-labels data instances (i.e., transforms a decision space) to improve prediction quality. It emphasizes knowledge generalization and confidence rather than classification accuracy. A confidence index incorporating classification accuracy, prediction error, impurities in the relabeled dataset, and cluster purities was designed. The proposed approach is illustrated with a binary outcome dataset and was successfully tested on the standard benchmark four UCI repository dataset as well as bladder cancer immunotherapy data. A subset of the most stable instances (i.e., 7% to 51% of the sample) with high confidence (i.e., between 64%-99.44%) was identified for each application along with most noisy instances. The domain experts and the extracted knowledge validated the relabeled instances and corresponding confidence indexes. The relabeling algorithm with some modifications can be applied to other medical, industrial, and service domains.

Resumo Limpo

relabel algorithm retriev noisi instanc binari outcom present relabel algorithm iter retriev select relabel data instanc ie transform decis space improv predict qualiti emphas knowledg general confid rather classif accuraci confid index incorpor classif accuraci predict error impur relabel dataset cluster puriti design propos approach illustr binari outcom dataset success test standard benchmark four uci repositori dataset well bladder cancer immunotherapi data subset stabl instanc ie sampl high confid ie identifi applic along noisi instanc domain expert extract knowledg valid relabel instanc correspond confid index relabel algorithm modif can appli medic industri servic domain

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