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Stacked Generalization of Random Forest and Decision Tree Techniques for Library Data Visualization
Stanley Ziweritin
computer-application
Citation
Stanley Ziweritin, “Stacked Generalization of Random Forest and Decision Tree Techniques for Library Data Visualization”, International Journal of Engineering and Applied Computer Science, vol. 04, no. 04, pp. 01-09, May. 2022.
computer-application
Abstract
The huge amount of library data stored in our modern research and statistic centers of organizations is springing up on daily bases. These databases grow exponentially in size with respect to time, it becomes exceptionally difficult to easily understand the behavior and interpret data with the relationships that exist between attributes. This exponential growth of data poses new organizational challenges like the conventional record management system infrastructure could no longer cope to give precise and detailed information about the behavior data over time. There is confusion and novel concern in selecting tools that can support and handle big data visualization that deals with multi-dimension. Viewing all related data at once in a database is a problem that has attracted the interest of data professionals with machine learning skills. This is a lingering issue in the data industry because the existing techniques cannot be used to remove or filter noise from relevant data and pad up missing values in order to get the required information. The aim is to develop a stacked generalization model that combines the functionality of random forest and decision tree to visualization library database visualization. In this paper, the random forest and decision tree techniques were employed to effectively visualize large amounts of school library data. The proposed system was implemented with a few lines of Python code to create visualizations that can help users at a glance understand and interpret the behavior of data and its relationships. The model was trained and tested to learn and extract hidden patterns of data with a cross-validation test. It combined the functionalities of both models to form a stacked generalization model that performed better than the individual techniques. The stacked model produced 95% followed by the RF which produced a 95% accuracy rate and 0.223600 RMSE error value in comparison with the DT which recorded an 80.00% success rate and 0.15990 RMSE value.
2.35
Impact Factor
hassan kazemian UK
british journal
STANLEY ZIWERITIN
Mr. Stanley Ziweritin is working as a lecturer in the Department of Estate Management and Valuation at the School of Environmental Design and Technology, Akanu Ibiam Federal Polytechnic, Unwana-Afikpo, Ebonyi State. He holds HND (Computer Science) from Rivers (Now Kenule Benson Saro-Wiwa) State Polytechnic, Bori. PGD (Computer Science) and M.Sc (Computer Science) from the University of Port Harcourt (UPH) respectively. His research interest revolves around: Artificial Intelligence (AI), Deep Machine Learning (DML), Natural Language Processing (NLP), Data Mining, Data Visualization, Algorithms, Database System Design, and Programming. He has published in several international journals. He is a registered member of the Computer Professionals (Registration Council) of Nigeria (CPN).