A hybrid GP-KNN imputation for symbolic regression with missing values

26Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In data science, missingness is a serious challenge when dealing with real-world data sets. Although many imputation approaches have been proposed to tackle missing values in machine learning, most studies focus on the classification task rather than the regression task. To the best of our knowledge, no study has been conducted to investigate the use of imputation methods when performing symbolic regression on incomplete real-world data sets. In this work, we propose a new imputation method called GP-KNN which is a hybrid method employing two concepts: Genetic Programming Imputation (GPI) and K-Nearest Neighbour (KNN). GP-KNN considers both the feature and instance relevance. The experimental results show that the proposed method has a better performance comparing to state-of-the-art imputation methods in most of the considered cases with respect to both imputation accuracy and symbolic regression performance.

Cite

CITATION STYLE

APA

Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2018). A hybrid GP-KNN imputation for symbolic regression with missing values. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11320 LNAI, pp. 345–357). Springer Verlag. https://doi.org/10.1007/978-3-030-03991-2_33

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free