Abstract
Missing values in datasets are caused by various reasons. These missing values in datasets adversely impact the performance of data mining (DM) algorithms. These values can be ignored if there are large number of instances in the dataset. However, deleting the records containing missing values in smaller sized datasets can lead to improper classification or predictions by the data mining algorithms. Several methods for finding missing values are present. Commonly used methods like replacing the missing values by the mean of the column, repeated value, are present. However, these techniques don’t approximately forecast the missing values. In our work, we have considered regression-based methodology for predicting the missing values. Results on stock dataset demonstrated that the polynomial regression (PR) model exhibited better performance when compared to linear regression (LR), quadratic regression (QR), pure-quadratic regression (PQR) and interactions regression (IR) models for prediction of missing values.
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Aditya Shastry, K., Sanjay, H.A., Praveen, M.S. (2022). Regression Based Data Pre-processing Technique for Predicting Missing Values. In: Shetty, N.R., Patnaik, L.M., Nagaraj, H.C., Hamsavath, P.N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Lecture Notes in Electrical Engineering, vol 789. Springer, Singapore. https://doi.org/10.1007/978-981-16-1338-8_9
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DOI: https://doi.org/10.1007/978-981-16-1338-8_9
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