Abstract
In real-world problems, data are generally characterized by their imperfection. One of the most common forms of imperfection is missing data. In fact, dealing with missing data remains a very important issue in data mining and knowledge discovery researches. A panoply of methods, addressing this problem, is proposed in the literature handling different types of data. In this work, we focus our study towards three methods which are KNN, MissForest, and EM algorithm. These methods are considered among the most efficient in different imputation problems. In the first part of this work, we present a brief state of the art of the used imputation methods and the strategy that we propose to use. In the second part, we provide a comparative study based on different criterion showing the efficiency of MissForest compared to the other methods and we demonstrate that the combination is preferable to improve the imputation of continuous data instead of using them individually.
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Ben Hariz, N., Khoufi, H., Zagrouba, E. (2017). On Combining Imputation Methods for Handling Missing Data. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_20
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DOI: https://doi.org/10.1007/978-3-319-60042-0_20
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