Abstract
Missing data can produce a significant risk of yielding inaccurate deductions due to the lack of critical attribute values. In gene expression data, missing values are prominent because of the apparatus error, inefficient techniques used for measurements, abraded slides, etc. These missing values create issues in visualizing gene features and other biological studies. Hence, for the study of the structural information of the gene expressions, efficient prediction of missing values becomes crucial. Consequently, the problem of accurate imputation of missing values has obtained considerable interest from researchers. To address this challenge, this paper presents a hybrid model used for imputing missing values in the gene expression dataset. The proposed model utilizes a machine learning-based ensemble technique known as Extra tree regression and genetic algorithm to optimize parameters of the K-Means clustering algorithm. Then optimized K-Means algorithm is used to estimate missing values in the dataset. This paper discusses the impact of distinct missing ratios on the performance of the proposed model and also compares accuracy with baseline models.
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Yadav, A., Rasool, A., Dubey, A., Khare, N. (2023). A Hybrid Approach for Missing Data Imputation in Gene Expression Dataset Using Extra Tree Regressor and a Genetic Algorithm. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_12
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DOI: https://doi.org/10.1007/978-981-99-0047-3_12
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