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A data-driven knowledge acquisition system: an end-to-end knowledge engineering process for generating production rules

Citation

Ali, M and Ali, R and Khan, WA and Han, SC and Bang, J and Hur, T and Kim, D and Lee, S and Kang, BH, A data-driven knowledge acquisition system: an end-to-end knowledge engineering process for generating production rules, IEEE Access, 6 pp. 15587-15607. ISSN 2169-3536 (2018) [Refereed Article]


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Copyright Statement

Copyright 2018 IEEE

DOI: doi:10.1109/ACCESS.2018.2817022

Abstract

Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with large amounts of data has received a lot of attention in the field recently, and a number of methodologies have been proposed to extract insights from data in an automated or semi-automated manner. However, these methodologies generally target a specific aspect of the data mining process, such as data acquisition, data preprocessing, or data classification. However, a comprehensive knowledge acquisition method is crucial to support the end-to-end knowledge engineering process. In this paper, we introduce a knowledge acquisition system that covers all major phases of the cross-industry standard process for data mining. Acknowledging the importance of an end-to-end knowledge engineering process, we designed and developed an easy-to-use data-driven knowledge acquisition tool (DDKAT). The major features of the DDKAT are: (1) a novel unified features scoring approach for data selection; (2) a user-friendly data processing interface to improve the quality of the raw data; (3) an appropriate decision tree algorithm selection approach to build a classification model; and (4) the generation of production rules from various decision tree classification models in an automated manner. Furthermore, two diabetes studies were performed to assess the value of the DDKAT in terms of user experience. A total of 19 experts were involved in the first study and 102 students in the artificial intelligence domain were involved in the second study. The results showed that the overall user experience of the DDKAT was positive in terms of its attractiveness, as well as its pragmatic and hedonic quality factors.

Item Details

Item Type:Refereed Article
Keywords:knowledge engineering, data mining, features ranking, algorithm selection, decision tree, production rule, user experience
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Pattern Recognition and Data Mining
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Information Processing Services (incl. Data Entry and Capture)
Author:Ali, M ( Maqbool Ali)
Author:Kang, BH (Professor Byeong Kang)
ID Code:125191
Year Published:2018
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2018-04-06
Last Modified:2018-05-04
Downloads:13 View Download Statistics

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