Suitable Domains for Using Ordered Attribute Trees to Impute Missing Values
Summary :
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 attribute values 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 the results 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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- IEICE TRANSACTIONS on Information Vol.E84-D No.2 pp.262-270
- Publication Date
- 2001/02/01
- Publicized
- Online ISSN
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- Type of Manuscript
- PAPER
- Category
- Databases
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Oscar-Ortega LOBO, Masayuki NUMAO, "Suitable Domains for Using Ordered Attribute Trees to Impute Missing Values" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 2, pp. 262-270, February 2001, doi: .
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 attribute values 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 the results 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_2_262/_p
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@ARTICLE{e84-d_2_262,
author={Oscar-Ortega LOBO, Masayuki NUMAO, },
journal={IEICE TRANSACTIONS on Information},
title={Suitable Domains for Using Ordered Attribute Trees to Impute Missing Values},
year={2001},
volume={E84-D},
number={2},
pages={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 attribute values 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 the results 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.},
keywords={},
doi={},
ISSN={},
month={February},}
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TY - JOUR
TI - Suitable Domains for Using Ordered Attribute Trees to Impute Missing Values
T2 - IEICE TRANSACTIONS on Information
SP - 262
EP - 270
AU - Oscar-Ortega LOBO
AU - Masayuki NUMAO
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2001
AB - 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 attribute values 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 the results 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.
ER -
English
