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Modulo 9 model-based learning for missing data imputation
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DOI：10.1016/j.asoc.2021.107167.png)
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Abstract
En 中文
Missing Values Management is one of the challenges faced by Data Analysts. Therefore, the creation of effective data models will be the right decision for missing data imputation. However, learning, training, and Data Analysis must be implemented through machine learning algorithms. Missing Data is a problem with no feedback or variables. This problem (missing data) can result in serious Data Analysis, which may eventually lead to erroneous conclusions. This research paper first studies how missing data can affect Machine Learning Algorithms, and decision-making based on the Data Analysis's output. Secondly, it proposes Modulo 9 as a novel method for handling missing data problems. The proposed novel method is assessed with wide-ranging experiments compared with robust Machine Learning techniques such as Support Vector Machine (SVM) Algorithm, Linear Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Classifier (SVC), Linear Support Vector Classifier (LSVC), Random Forest Classifier (RFC), Decision Tree Regressor (DTR), Deletion Method, Multi-Layer Perceptron (MLP), and the Mean Value. The results show that the novel method outperforms the eleven (11) existing methods. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Data quality
Machine learning algorithms
Missing data
Modulo 9
Journal
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6.6
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1.4W
Citations:
4.8W
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