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Applied Artificial Intelligence
An International Journal
Volume 36, 2022 - Issue 1
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Research Article

Analysis of Proposed and Traditional Boosting Algorithm with Standalone Classification Methods for Classifying Gene Expresssion Microarray Data Using a Reject Option

Adil Aslam Mira Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, PakistanView further author information
,
Lal Hussaina Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan;b Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, Azad Kashmir, PakistanCorrespondence[email protected]
View further author information
,
Muhammad Hammad Waseemb Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, Azad Kashmir, PakistanView further author information
,
Amjad Aldweeshc College of Computer science and information technology, Shaqra University, Sahqra, Saudi ArabiaView further author information
,
Saim Rasheedd Department of Information Technology, Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi ArabiaView further author information
,
El Sayed Yousefe Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi Arabia;f Physics Dep. Faculty of Science, King Khalid University, Abha, Saudi ArabiaView further author information
,
Malik Sajjad Ahmed Nadeema Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, PakistanView further author information
&
Elsayed Tag Elding Faculty of Engineering and Technology, Future University in Egypt New Cairo, New Cairo, EgyptView further author information
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Article: 2151171 | Received 26 Dec 2021, Accepted 18 Nov 2022, Published online: 30 Nov 2022

Figures & data

Figure 1. Probabilistic Boosting Method.

Figure 1. Probabilistic Boosting Method.

Figure 2. Data Split Methodology.

Figure 2. Data Split Methodology.

Figure 3. Comparison of Classification Accuracy of Proposed Method with traditional method and simple Tree base classifier using Colon Cancer Data.

Figure 3. Comparison of Classification Accuracy of Proposed Method with traditional method and simple Tree base classifier using Colon Cancer Data.

Figure 4. Comparison of Classification Accuracy of Proposed Method with traditional method and simple Tree base classifier using Leukemia Data.

Figure 4. Comparison of Classification Accuracy of Proposed Method with traditional method and simple Tree base classifier using Leukemia Data.

Figure 5. Comparison of Classification Accuracy of Proposed Method with traditional method using SVM as a base Classifier and simple SVM base classifier using lymphoid cancer Data.

Figure 5. Comparison of Classification Accuracy of Proposed Method with traditional method using SVM as a base Classifier and simple SVM base classifier using lymphoid cancer Data.

Figure 6. Comparison of Classification Accuracy of Proposed Method with traditional method using SVM as a base classifier and simple SVM base classifier using lymphoid cancer data.

Figure 6. Comparison of Classification Accuracy of Proposed Method with traditional method using SVM as a base classifier and simple SVM base classifier using lymphoid cancer data.

Figure 7. Analysis of proposed method with other methods using Leukemia Data.

Figure 7. Analysis of proposed method with other methods using Leukemia Data.

Figure 8. Analysis of proposed method with other methods using colon cancer data.

Figure 8. Analysis of proposed method with other methods using colon cancer data.

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