A comparison of three closest fit approaches to missing attribute values in preterm birth data

Grzymała-Busse, Jerzy W.; Grzymala-Busse, Witold J.; Goodwin, Linda

doi:10.1002/int.10012

Cited by 21 publications

(14 citation statements)

References 8 publications

(7 reference statements)

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“…The global closest fit method replaces a missing feature value by the known value in another observation that resembles as much as possible the instance with the missing attribute values (Grzymala-Busse et al 2002). The closest fit case is the instance which has the minimum distance to the instance with the missing values.…”

Section: Global Closest Fit (Gcf)mentioning

confidence: 99%

AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Zhou

Lai

2016

Comput Econ

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Very little existing research in corporate bankruptcy prediction discusses modeling where there are missing values. This paper investigates AdaBoost models for corporate bankruptcy prediction with missing data. Three AdaBoost models integrated with different imputation methods are tested on two data sets with very different sample sizes. The experimental results show that the AdaBoost algorithm combined with imputation methods has strong predictive accuracy in both data sets and it is a useful alternative for bankruptcy prediction with missing data.

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Section: Global Closest Fit (Gcf)mentioning

confidence: 99%

AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Zhou

Lai

2016

Comput Econ

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“…(h) Global Closest Fit: This substitutes a missing attribute value by the known value in another case that matches the affected object the closest in terms of the remaining attributes [7]. A drawback of this method is that some missing values may remain because no object matches the target object for any of the attributes.…”

Section: B Past Approaches For Handling Missing Attribite Valuesmentioning

confidence: 99%

Handling data incompleteness using Rough Sets on multiple decision systems

Lata

Chakraverty

2014

2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)

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A practical problem that arises in data analysis is to handle missing attribute values in an information system that has suffered degradation, so as to retain its quality. In this paper, we present a new Rough Set (RS) based approach to deal with incomplete data. The core idea is to tap the redundant information garnered from different databases that share common attributes. The attribute suffering missing entries in a deficient database is recast as a decision attribute in another reference database. The tenets of RS theory are then applied to derive rules that predict the missing values. Experimental results on pairs of two different pairs of related databases taken from the UCI repository reveal that our approach could predict missing values with a high degree of accuracy giving an average error of 15.75%.

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“…The global closes fit method [8] is based on replacing a missing attribute value by the known value in another case that resembles as much as possible the case with the missing attribute value. In searching for the closest fit case we compare two vectors of attribute values, one vector corresponds to the case with a missing attribute value, the other vector is a candidate for the closest fit.…”

Section: Global Closest Fitmentioning

confidence: 99%

A comparison of traditional and rough set approaches to missing attribute values in data mining

Grzymała-Busse¹

2009

Data Mining X

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Real-life data sets are often incomplete, i.e., some attribute values are missing. In this paper we compare traditional, frequently used methods of handling missing attribute values, which are based on preprocessing, with another class of methods dealing with missing attribute values in which rule induction is performed directly on incomplete data sets, i.e., handling missing attribute values and rule induction are conducted concurrently. In our experiments four traditional methods of handling missing attribute values were applied: Most Common Value, Concept Most Common Value, Closest Fit, and Concept Closest Fit. Both Closest Fit methods were enhanced by a rough set approach to missing attribute values. On the same typical data sets experiments were conducted using three different rough-set interpretations of missing attribute values: lost values, "do not care" conditions and attribute-concept values using the MLEM2 rule induction algorithm, based on rough set theory. The best method is the Concept Closest Fit enhanced by interpreting remaining missing attribute values as lost values.

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A comparison of three closest fit approaches to missing attribute values in preterm birth data

Cited by 21 publications

References 8 publications

AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Handling data incompleteness using Rough Sets on multiple decision systems

A comparison of traditional and rough set approaches to missing attribute values in data mining

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