INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.14, No.5, Oct. 2022

A Comparison of Missing Value Imputation Techniques on Coupon Acceptance Prediction

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Author(s)

Rahin Atiq, Farzana Fariha, Mutasim Mahmud, Sadman S. Yeamin, Kawser I. Rushee, Shamsur Rahim

Index Terms

Missing Value Imputation Technique;Imbalanced Dataset;SMOTE (Synthetic Minority Over-sampling Technique);KNN;Mice;Mean;Naïve Bayes;Gradient Boosted Tree;Deep Learning;Random Forest;Lo-gistic Regression;Classifier

Abstract

The In-Vehicle Coupon Recommendation System is a type of coupon used to represent an idea of different driving scenarios to users. Basically, with the help of presenting the scenarios, the people’s opinion is taken on whether they will accept the coupon or not. The coupons offered in the survey were for Bar, Coffee Shop, Restaurants, and Take Away. The dataset consists of various attributes that capture precise information about the clients to give a coupon recommendation. The dataset is significant to shops to determine whether the coupons they offer are benefi-cial or not, depending on the different characteristics and scenarios of the users. A major problem with this dataset was that the dataset was imbalanced and mixed with missing values. Handling the missing values and imbalanced class problems could affect the prediction results. In the paper, we analysed the impact of four different imputation techniques (Frequent value, mean, KNN, MICE) to replace the missing values and use them to create prediction mod-els. As for models, we applied six classifier algorithms (Naive Bayes, Deep Learning, Logistic Regression, Decision Tree, Random Forest, and Gradient Boosted Tree). This paper aims to analyse the impact of the imputation techniques on the dataset alongside the outcomes of the classifiers to find the most accurate model among them. So that shops or stores that offer coupons or vouchers would get a real idea about their target customers. From our research, we found out that KNN imputation with Deep Learning classifier gave the most accurate outcome for prediction and false-negative rate.

Cite This Paper

Rahin Atiq, Farzana Fariha, Mutasim Mahmud, Sadman S. Yeamin, Kawser I. Rushee, Shamsur Rahim, "A Comparison of Missing Value Imputation Techniques on Coupon Acceptance Prediction", International Journal of Infor-mation Technology and Computer Science(IJITCS), Vol.14, No.5, pp.15-25, 2022. DOI:10.5815/ijitcs.2022.05.02

Reference

[1]T. Wang, C. Rudin, F. Doshi-Velez, Y. Liu and E. Klampfl, "A Bayesian Framework for Learning Rule Sets for Interpreta-ble," Journal of Machine Learning Research, p. 37, 2017. 

[2]S. I. Khan and A. S. M. L. Hoque, "SICE: an improved missing data imputation technique," Journal of Big Data, 2020. 

[3]K. Moorthy, M. H. Ali and M. A. Ismail, "An Evaluation of Machine Learning Algorithms for Missing Values Imputa-tion," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 12S2, October 2019. 

[4]C.-H. Liu, C.-F. Tsai, K.-L. Sue and M.-W. Huang, "The Feature Selection Effect on Missing Value Imputation of Medical Datasets," Multidisciplinary Digital Publishing Institute, March 2020. 

[5]D. Bertsimas, C. Pawlowski and Y. D. Zhuo, "From Predictive Methods to Missing Data Imputation: An Optimization Approach," Journal of Machine Learning Research, 2018. 

[6]B. Conroy, L. Eshelman, C. Potes and M. Xu-Wilson, "A dynamic ensemble approach to robust classification in the pres-ence of missing data," Springer, 2015. 

[7]U. M. L. Repository, "in-vehicle coupon recommendation Data Set," [Online]. Available: https://archive.ics.uci.edu/ml/datasets/in-vehicle+coupon+recommendation.

[8]M. G. Rahman and M. Z. Islam, "Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques," Knowledge-Based Systems, November 2013. 

[9]K. Grace-Martin, "The Analysis Factor," October 2012. [Online]. Available: https://www.theanalysisfactor.com/mean-imputation/.

[10]S. Buuren and C. Groothuis-Oudshoorn, "MICE: Multivariate Imputation by Chained Equation," Journal of Statistical Software, 2011. 

[11]M. J. Azur, E. A. Stuart, C. Frangakis and P. J. Leaf, "Multiple imputation by chained equations: what is it and how does it work?," International Journal of Methods in Psychiatric Research, 2011. 

[12]L. Breiman, "Random Forests," Machine Learning, 2001. 

[13]J. Ali, R. Khan, N. Ahmad and I. Maqsood, "Random Forests and Decision Trees," International Journal of Computer Sci-ence Issues(IJCSI), 2012. 

[14]M. Chandrasekaran, "Capital One," 8 November 2021. [Online]. Available: https://www.capitalone.com/tech/machine-learning/what-is-logistic-regression/.

[15]Gaurav, "Machine Learning Plus," June 2021. [Online]. Available: https://www.machinelearningplus.com/machine-learning/an-introduction-to-gradient-boosting-decision-trees/.

[16]C. F. Costa and M. A. Nascimento, "IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures," in International Symposium on Intelligent Data Analysis, 2016.