An Evaluation of Machine Learning Algorithms for Missing Values Imputation
Kohbalan Moorthy1, Mohammed Hasan Ali2, Mohd Arfian Ismail3, Chan Weng Howe4, Mohd Saberi Mohamad5, Safaai Deris6
1Kohbalan Moorthy, Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Kuantan, Malaysia.
2Mohammed Hasan Ali, Department of Computer Techniques Engineering, Faculty of Information Technology, Imam Ja’afar Al-sadiq University, Najaf, Iraq.
3Mohd Arfian Ismail, Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Kuantan, Malaysia.
4Chan Weng Howe, Artificial Intelligence and Bioinformatics Group (IAIBG), Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
5Mohd Saberi Mohamad, Artificial Intelligence and Bioinformatics Group (IAIBG), Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
6Safaai Deris, Artificial Intelligence and Bioinformatics Group (IAIBG), Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
Manuscript received on 08 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 415-420 | Volume-8 Issue-12S2 October 2019 | Retrieval Number: L108110812S219/2019©BEIESP | DOI: 10.35940/ijitee.L1081.10812S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In gene expression studies missing values have been a common problem. It has an important consequence on the explanation of the final data. Numerous Bioinformatics examination tools that are used for cancer prediction includes the dataset matrix. Hence, it is necessary to resolve this problem of missing values imputation. Our research paper presents a review of missing values imputation approaches. It represents the research and imputation of missing values in gene expression data. By using the local or global correlation of the data we focus mostly on the contrast of the algorithms. We considered the algorithms in a global, hybrid, local, and knowledge-based technique. Additionally, we presented the different approaches with a suitable assessment. The purpose of our review article is to focus on the developments of current techniques. For scientists rather applying different or newly develop algorithms with the identical functional goal. We want an adaptation of algorithms to the characteristics of the data”.
Keywords: Missing Value Imputation, Gene Expression Data, Microarray Data, Cancer Informatics, Computational Intelligence.
Scope of the Article: Machine Learning