Artif Intell Med - Multi-test decision tree and its application to microarray data classification.

Tópicos

{ method(1969) cluster(1462) data(1082) }
{ decis(3086) make(1611) patient(1517) }
{ structur(1116) can(940) graph(676) }
{ gene(2352) biolog(1181) express(1162) }
{ problem(2511) optim(1539) algorithm(950) }
{ learn(2355) train(1041) set(1003) }
{ data(3008) multipl(1320) sourc(1022) }
{ featur(3375) classif(2383) classifi(1994) }
{ model(2220) cell(1177) simul(1124) }
{ method(1219) similar(1157) match(930) }
{ design(1359) user(1324) use(1319) }
{ general(901) number(790) one(736) }
{ import(1318) role(1303) understand(862) }
{ patient(2837) hospit(1953) medic(668) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ howev(809) still(633) remain(590) }
{ perform(1367) use(1326) method(1137) }
{ model(2656) set(1616) predict(1553) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ bind(1733) structur(1185) ligand(1036) }
{ assess(1506) score(1403) qualiti(1306) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
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{ studi(1119) effect(1106) posit(819) }
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{ featur(1941) imag(1645) propos(1176) }
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{ health(3367) inform(1360) care(1135) }
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{ 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) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity.METHODS: We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions.RESULTS: Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature.CONCLUSION: This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts.

Resumo Limpo

jectiv desir properti tool use investig biolog data easi understand model predict decis decis tree particular promis regard due comprehens natur resembl hierarch process human decis make howev exist algorithm learn decis tree tendenc underfit gene express data main aim work improv perform stabil decis tree small increas complexitymethod propos multitest decis tree mtdt main contribut applic sever univari test nontermin node decis tree also search altern lowerrank featur order obtain stabl reliabl predictionsresult experiment valid perform sever reallif gene express dataset comparison result eight classifi show mtdt statist signific higher accuraci popular decis tree classifi high competit ensembl learn algorithm propos solut manag outperform baselin algorithm dataset averag studi perform one dataset show discov gene use mtdt classif model support biolog evid literatureconclus paper introduc new type decis tree suitabl solv biolog problem mtdts relat easi analyz much power model high dimension microarray data popular counterpart

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