Artif Intell Med - An evaluation of heuristics for rule ranking.

Tópicos

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{ learn(2355) train(1041) set(1003) }
{ system(1976) rule(880) can(841) }
{ model(2341) predict(2261) use(1141) }
{ group(2977) signific(1463) compar(1072) }
{ control(1307) perform(991) simul(935) }
{ clinic(1479) use(1117) guidelin(835) }
{ howev(809) still(633) remain(590) }
{ research(1218) medic(880) student(794) }
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{ use(1733) differ(960) four(931) }
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{ treatment(1704) effect(941) patient(846) }
{ method(984) reconstruct(947) comput(926) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ data(3008) multipl(1320) sourc(1022) }
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{ compound(1573) activ(1297) structur(1058) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
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{ drug(1928) target(777) effect(648) }
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{ survey(1388) particip(1329) question(1065) }
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{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: To evaluate and compare the performance of different rule-ranking algorithms for rule-based classifiers on biomedical datasets.METHODOLOGY: Empirical evaluation of five rule ranking algorithms on two biomedical datasets, with performance evaluation based on ROC analysis and 5 ? 2 cross-validation.RESULTS: On a lung cancer dataset, the area under the ROC curve (AUC) of, on average, 14267.1 rules was 0.862. Multi-rule ranking found 13.3 rules with an AUC of 0.852. Four single-rule ranking algorithms, using the same number of rules, achieved average AUC values of 0.830, 0.823, 0.823, and 0.822, respectively. On a prostate cancer dataset, an average of 339265.3 rules had an AUC of 0.934, while 9.4 rules obtained from multi-rule and single-rule rankings had average AUCs of 0.932, 0.926, 0.925, 0.902 and 0.902, respectively.CONCLUSION: Multi-variate rule ranking performs better than the single-rule ranking algorithms. Both single-rule and multi-rule methods are able to substantially reduce the number of rules while keeping classification performance at a level comparable to the full rule set.

Resumo Limpo

jectiv evalu compar perform differ rulerank algorithm rulebas classifi biomed datasetsmethodolog empir evalu five rule rank algorithm two biomed dataset perform evalu base roc analysi crossvalidationresult lung cancer dataset area roc curv auc averag rule multirul rank found rule auc four singlerul rank algorithm use number rule achiev averag auc valu respect prostat cancer dataset averag rule auc rule obtain multirul singlerul rank averag auc respectivelyconclus multivari rule rank perform better singlerul rank algorithm singlerul multirul method abl substanti reduc number rule keep classif perform level compar full rule set

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