Citation

Mohd Zebaral Hoque, Jesmeen and Hossen, Abid and Hossen, Jakir and Raja, Emerson and Thangavel, Bhuvaneswari and Sayeed, Shohel and Hossen, Abid (2019) AUTO-CDD: automatic cleaning dirty data using machine learning techniques. TELKOMNIKA, 17 (4). pp. 2076-2086. ISSN 1693-6930

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Abstract

Cleaning the dirty data has become very critical significance for many years, especially in medical sectors. This is the reason behind widening research in this sector. To initiate the research, a comparison between currently used functions of handling missing values and Auto-CDD is presented. The developed system will guarantee to overcome processing unwanted outcomes in data Analytical process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally, it concludes that this process will help to get clean data for further analytical process.

Item Type:	Article
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	27 Apr 2022 01:49
Last Modified:	27 Apr 2022 01:49
URII:	http://shdl.mmu.edu.my/id/eprint/9406

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