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Titolo:
Suitable domains for using ordered attribute trees to impute missing values
Autore:
Lobo, OO; Numano, M;
Indirizzi:
Univ Antioquia, Fac Engn, Dept Syst Engn, Medellin, Colombia Univ Antioquia Medellin Colombia gn, Dept Syst Engn, Medellin, Colombia Tokyo Inst Technol, Grad Sch Informat Sci & Engn, Dept Comp Sci, Tokyo 1528552, Japan Tokyo Inst Technol Tokyo Japan 1528552 pt Comp Sci, Tokyo 1528552, Japan
Titolo Testata:
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
fascicolo: 2, volume: E84D, anno: 2001,
pagine: 262 - 270
SICI:
0916-8532(200102)E84D:2<262:SDFUOA>2.0.ZU;2-D
Fonte:
ISI
Lingua:
ENG
Soggetto:
ASSOCIATION RULES;
Keywords:
machine learning; incomplete data; decision trees; mutual information analysis; ordered attribute trees method;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
23
Recensione:
Indirizzi per estratti:
Indirizzo: Lobo, OO Univ Antioquia, Fac Engn, Dept Syst Engn, POB 1226, Medellin, Colombia Univ Antioquia POB 1226 Medellin Colombia 6, Medellin, Colombia
Citazione:
O.O. Lobo e M. Numano, "Suitable domains for using ordered attribute trees to impute missing values", IEICE T INF, E84D(2), 2001, pp. 262-270

Abstract

Using decision trees to fill the missing values in data has been shown experimentally to be useful in some domains. However, this is not the general case. In other domains, using decision trees for imputing missing attributevalues does not outperform other methods. Trying to identify the reasons behind the success or failure of the various methods for filling missing values on different domains can be useful for deciding the technique to be used when learning concepts from a new domain with missing values. This paper presents a technique by which to approach to previous goal and presents theresults of applying the technique on predicting the success or failure of a method that uses decision trees to fill the missing values in an ordered manner. Results are encouraging because the obtained decision tree is simple and it can even provide hints for further improvement on the use of decision trees to impute missing attribute values.

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Documento generato il 09/04/20 alle ore 20:00:36