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https://publication.npru.ac.th/jspui/handle/123456789/1931
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DC Field | Value | Language |
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dc.contributor.author | Namkaew, Aphisit | - |
dc.contributor.author | Hengpraprohm, Kairung | - |
dc.contributor.author | Hengpraprohm, Supojn | - |
dc.date.accessioned | 2023-11-06T09:27:54Z | - |
dc.date.available | 2023-11-06T09:27:54Z | - |
dc.date.issued | 2023-07-14 | - |
dc.identifier.uri | 978-974-7063-43-1 | - |
dc.identifier.uri | https://publication.npru.ac.th/jspui/handle/123456789/1931 | - |
dc.description.abstract | The objectives of this research are 1) to compare the efficiency of lung cancer data classification using four data mining techniques: Decision Tree, Naive Bayes, Support Vector Machine, and Artificial Neural Network; 2) to improve the data classification model for lung cancer datasets which consisted of 16 columns and 310 rows. The performance was tested by the 10-Fold Cross Validation method using Rapid Miner Studio 9.10 software. The results show that the Artificial Neural Network gave the best efficiency with an accuracy of 89.25 %, recall of 90.12 %, and precision of 97.33 %. After that, the efficiency of data classification of the Artificial Neural Network is improved by using the majority vote ensemble method. It improved the efficiency with a classification accuracy of 91.40 % and a recall of 92.59 % and a precision is 97.40 %, respectively. | en_US |
dc.publisher | The 15th NPRU National Academic Conference Nakhon Pathom Rajabhat University | en_US |
dc.relation.ispartofseries | Proceedings of the 15th NPRU National Academic Conference;557 | - |
dc.subject | data mining | en_US |
dc.subject | decision tree | en_US |
dc.subject | naive bays | en_US |
dc.subject | support vector machine | en_US |
dc.subject | data classification | en_US |
dc.title | Improving the efficiency of lung cancer data classification using ensemble technique | en_US |
dc.title.alternative | การเพิ่มประสิทธิภาพในการจาแนกข้อมูลโรคมะเร็งปอดด้วยวิธีการกลุ่มก้อนข้อมูล | en_US |
dc.type | Article | en_US |
Appears in Collections: | Proceedings of the 15th NPRU National Academic Conference |
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