Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights
Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7815418 |
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| _version_ | 1832547718621298688 |
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| author | Xiaoqun Liao Shah Nazir Junxin Shen Bingliang Shen Sulaiman Khan |
| author_facet | Xiaoqun Liao Shah Nazir Junxin Shen Bingliang Shen Sulaiman Khan |
| author_sort | Xiaoqun Liao |
| collection | DOAJ |
| description | Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours (KNN), decision rules (DR), decomposition tree (DT), and local transfer function classifier (LTF-C) for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem. |
| format | Article |
| id | doaj-art-8eaae53b4ae14393a68d4a2c81182964 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8eaae53b4ae14393a68d4a2c811829642025-02-03T06:43:46ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/78154187815418Rough Set Approach toward Data Modelling and User Knowledge for Extracting InsightsXiaoqun Liao0Shah Nazir1Junxin Shen2Bingliang Shen3Sulaiman Khan4Information and Network Center, Xi’an University of Science and Technology, Xi’an 710054, ChinaDepartment of Computer Science, University of Swabi, Swabi, PakistanFaculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan 650093, ChinaFaculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan 650093, ChinaDepartment of Computer Science, University of Swabi, Swabi, PakistanInformation is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours (KNN), decision rules (DR), decomposition tree (DT), and local transfer function classifier (LTF-C) for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem.http://dx.doi.org/10.1155/2021/7815418 |
| spellingShingle | Xiaoqun Liao Shah Nazir Junxin Shen Bingliang Shen Sulaiman Khan Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights Complexity |
| title | Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights |
| title_full | Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights |
| title_fullStr | Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights |
| title_full_unstemmed | Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights |
| title_short | Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights |
| title_sort | rough set approach toward data modelling and user knowledge for extracting insights |
| url | http://dx.doi.org/10.1155/2021/7815418 |
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