Record Details

Does Removing/Replacing Missing Values Improve The Models' Classification Performances?

International Journal of Management & Information Systems

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Field Value
 
Title Does Removing/Replacing Missing Values Improve The Models' Classification Performances?
 
Creator Zurada, Jozef
 
Subject Computer Information Systems
Classification Models; Credit Scoring Context; Missing Values Replacement/Removal; Improved Predictive Accuracy
 
Description The paper explores the effect of removing/replacing missing values on the classification performance of several models. The original data set, which contains a relatively large number of missing values, comes from the credit scoring context. This data set was not used to build the models, but it was converted to five other data sets with missing values either removed or replaced using different techniques. The models were built and tested on the five data sets. Preliminary computer simulation showed that the models created and tested on the four data sets in which missing values were replaced exhibited significantly better predictive performance than the model built and tested on the data set with missing values removed.
 
Publisher The Clute Institute
 
Date 2012-07-09
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://www.cluteinstitute.com/ojs/index.php/IJMIS/article/view/7073
10.19030/ijmis.v16i3.7073
 
Source International Journal of Management & Information Systems (IJMIS); Vol 16 No 3 (2012); 215-220
2157-9628
1546-5748
10.19030/ijmis.v16i3
 
Language eng
 
Relation https://www.cluteinstitute.com/ojs/index.php/IJMIS/article/view/7073/7147