A novel algorithm for imputing the missing values in incomplete datasets

In today’s world, our heavy reliance on digital devices for data collection has become the norm. However, when these devices fail, there is a significant risk of losing valuable information, making data mining an arduous task for data analysts. The presence of a substantial amount of missing data wi...

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Published in:	Iran Journal of Computer Science (Online) Vol. 7; no. 1; pp. 13 - 23
Main Authors:	Bhagat, Hutashan Vishal, Singh, Manminder
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.03.2024 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computer Science Computer Systems Organization and Communication Networks Data analysis Data collection Data mining Data Structures and Information Theory Datasets Mathematics of Computing Missing data Real time Root-mean-square errors Software Engineering/Programming and Operating Systems Theory of Computation Incomplete datasets Data missingness Missing values Missingness mechanisms Imputation Imputing values Data imputation model Root mean square error
ISSN:	2520-8438, 2520-8446
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Abstract	In today’s world, our heavy reliance on digital devices for data collection has become the norm. However, when these devices fail, there is a significant risk of losing valuable information, making data mining an arduous task for data analysts. The presence of a substantial amount of missing data within a dataset leads to inappropriate results and incomplete data analysis. Consequently, there is a pressing need to develop an algorithm capable of efficiently and accurately predicting missing values. This research paper introduces a new algorithm, known as the IMV-RE (imputing the missing values in real-time environment) algorithm, which is based on a novel splitting approach. The IMV-RE algorithm addresses the challenge of imputing various missing values within a dataset. To enhance prediction accuracy, the algorithm sets an upper limit for each class that contains missing values, providing valuable assistance in accurately predicting these values. To evaluate the efficacy of the IMV-RE algorithm, we conducted experiments using ten benchmark datasets comprising both numerical and mixed data. The results of our comparative analysis demonstrate that the proposed IMV-RE algorithm outperforms existing techniques in terms of sensitivity to accuracy, root mean square error (RMSE), and coefficient of determination ( R 2 ).
AbstractList	In today’s world, our heavy reliance on digital devices for data collection has become the norm. However, when these devices fail, there is a significant risk of losing valuable information, making data mining an arduous task for data analysts. The presence of a substantial amount of missing data within a dataset leads to inappropriate results and incomplete data analysis. Consequently, there is a pressing need to develop an algorithm capable of efficiently and accurately predicting missing values. This research paper introduces a new algorithm, known as the IMV-RE (imputing the missing values in real-time environment) algorithm, which is based on a novel splitting approach. The IMV-RE algorithm addresses the challenge of imputing various missing values within a dataset. To enhance prediction accuracy, the algorithm sets an upper limit for each class that contains missing values, providing valuable assistance in accurately predicting these values. To evaluate the efficacy of the IMV-RE algorithm, we conducted experiments using ten benchmark datasets comprising both numerical and mixed data. The results of our comparative analysis demonstrate that the proposed IMV-RE algorithm outperforms existing techniques in terms of sensitivity to accuracy, root mean square error (RMSE), and coefficient of determination (R2). In today’s world, our heavy reliance on digital devices for data collection has become the norm. However, when these devices fail, there is a significant risk of losing valuable information, making data mining an arduous task for data analysts. The presence of a substantial amount of missing data within a dataset leads to inappropriate results and incomplete data analysis. Consequently, there is a pressing need to develop an algorithm capable of efficiently and accurately predicting missing values. This research paper introduces a new algorithm, known as the IMV-RE (imputing the missing values in real-time environment) algorithm, which is based on a novel splitting approach. The IMV-RE algorithm addresses the challenge of imputing various missing values within a dataset. To enhance prediction accuracy, the algorithm sets an upper limit for each class that contains missing values, providing valuable assistance in accurately predicting these values. To evaluate the efficacy of the IMV-RE algorithm, we conducted experiments using ten benchmark datasets comprising both numerical and mixed data. The results of our comparative analysis demonstrate that the proposed IMV-RE algorithm outperforms existing techniques in terms of sensitivity to accuracy, root mean square error (RMSE), and coefficient of determination ( R 2 ).
Author	Singh, Manminder Bhagat, Hutashan Vishal
Author_xml	– sequence: 1 givenname: Hutashan Vishal surname: Bhagat fullname: Bhagat, Hutashan Vishal email: hutashan20@gmail.com organization: BV Raju Institute of Technology – sequence: 2 givenname: Manminder surname: Singh fullname: Singh, Manminder organization: Sant Longowal Institute of Engineering and Technology
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SubjectTerms	Algorithms Artificial Intelligence Computer Science Computer Systems Organization and Communication Networks Data analysis Data collection Data mining Data Structures and Information Theory Datasets Mathematics of Computing Missing data Real time Root-mean-square errors Software Engineering/Programming and Operating Systems Theory of Computation
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Title	A novel algorithm for imputing the missing values in incomplete datasets
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