Comput Biol Chem - A novel divide-and-merge classification for high dimensional datasets.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ learn(2355) train(1041) set(1003) }
{ high(1669) rate(1365) level(1280) }
{ motion(1329) object(1292) video(1091) }
{ method(1557) propos(1049) approach(1037) }
{ result(1111) use(1088) new(759) }
{ process(1125) use(805) approach(778) }
{ system(1976) rule(880) can(841) }
{ model(3480) simul(1196) paramet(876) }
{ structur(1116) can(940) graph(676) }
{ method(1219) similar(1157) match(930) }
{ perform(1367) use(1326) method(1137) }
{ cost(1906) reduc(1198) effect(832) }
{ activ(1138) subject(705) human(624) }
{ sequenc(1873) structur(1644) protein(1328) }
{ take(945) account(800) differ(722) }
{ design(1359) user(1324) use(1319) }
{ data(3963) clinic(1234) research(1004) }
{ age(1611) year(1155) adult(843) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ estim(2440) model(1874) function(577) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ 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) }
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{ framework(1458) process(801) describ(734) }
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{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ method(984) reconstruct(947) comput(926) }
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{ compound(1573) activ(1297) structur(1058) }
{ 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) }
{ 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) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ 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) }

Resumo

High dimensional datasets contain up to thousands of features, and can result in immense computational costs for classification tasks. Therefore, these datasets need a feature selection step before the classification process. The main idea behind feature selection is to choose a useful subset of features to significantly improve the comprehensibility of a classifier and maximize the performance of a classification algorithm. In this paper, we propose a one-per-class model for high dimensional datasets. In the proposed method, we extract different feature subsets for each class in a dataset and apply the classification process on the multiple feature subsets. Finally, we merge the prediction results of the feature subsets and determine the final class label of an unknown instance data. The originality of the proposed model is to use appropriate feature subsets for each class. To show the usefulness of the proposed approach, we have developed an application method following the proposed model. From our results, we confirm that our method produces higher classification accuracy than previous novel feature selection and classification methods.

Resumo Limpo

high dimension dataset contain thousand featur can result immens comput cost classif task therefor dataset need featur select step classif process main idea behind featur select choos use subset featur signific improv comprehens classifi maxim perform classif algorithm paper propos oneperclass model high dimension dataset propos method extract differ featur subset class dataset appli classif process multipl featur subset final merg predict result featur subset determin final class label unknown instanc data origin propos model use appropri featur subset class show use propos approach develop applic method follow propos model result confirm method produc higher classif accuraci previous novel featur select classif method

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