Neural Comput - Blocked 3?2 cross-validated t-test for comparing supervised classification learning algorithms.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ model(2341) predict(2261) use(1141) }
{ group(2977) signific(1463) compar(1072) }
{ estim(2440) model(1874) function(577) }
{ imag(1947) propos(1133) code(1026) }
{ can(774) often(719) complex(702) }
{ problem(2511) optim(1539) algorithm(950) }
{ general(901) number(790) one(736) }
{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
{ model(2220) cell(1177) simul(1124) }
{ sampl(1606) size(1419) use(1276) }
{ result(1111) use(1088) new(759) }
{ process(1125) use(805) approach(778) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ research(1085) discuss(1038) issu(1018) }
{ perform(1367) use(1326) method(1137) }
{ gene(2352) biolog(1181) express(1162) }
{ patient(1821) servic(1111) care(1106) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
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{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
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{ data(1714) softwar(1251) tool(1186) }
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{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
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{ risk(3053) factor(974) diseas(938) }
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{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
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{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In the research of machine learning algorithms for classification tasks, the comparison of the performances of algorithms is extremely important, and a statistical test of significance for generalization error is often used to perform it in the machine learning literature. In view of the randomness of partitions in cross-validation, a new blocked 3?2 cross-validation is proposed to estimate generalization error in this letter. We then conduct an analysis of variance of the blocked 3?2 cross-validated estimator. A relatively conservative variance estimator that considers the correlation between any two two-fold cross-validations, and was previously neglected in 5?2 cross-validated t and F-tests is put forward. A corresponding test using this variance estimator is presented to compare the performances of algorithms. Simulated results show that the performance of our test is comparable with that of 5?2 cross-validated tests but with less computation complexity.

Resumo Limpo

research machin learn algorithm classif task comparison perform algorithm extrem import statist test signific general error often use perform machin learn literatur view random partit crossvalid new block crossvalid propos estim general error letter conduct analysi varianc block crossvalid estim relat conserv varianc estim consid correl two twofold crossvalid previous neglect crossvalid t ftest put forward correspond test use varianc estim present compar perform algorithm simul result show perform test compar crossvalid test less comput complex

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