An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer.
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| Title: | An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer. |
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| Authors: | Chatterjee, Subhashis1 (AUTHOR), Das, Ananya1 (AUTHOR) ananyadas16@gmail.com |
| Source: | Soft Computing - A Fusion of Foundations, Methodologies & Applications. Jun2023, Vol. 27 Issue 11, p7147-7178. 32p. |
| Subjects: | Multiple criteria decision making, Evolutionary algorithms, Cancer diagnosis, Decision making, Fuzzy logic, Early detection of cancer, Fuzzy decision making, Fuzzy systems |
| Abstract: | Breast cancer is a life-threatening and consequential disease due to its invasive and proliferative trait, predominantly found in women. Early detection of the cancer is a significant contributor to improved mortality and hence is an area of keen focus for ongoing researches. However, developing a technique to diagnose the severity of the patients at an early stage is a challenging task. Manual diagnostic techniques are time-consuming and result in inaccurate diagnosis of breast cancer. Prompted by these facts, a quantum optimized rule-base generated automated framework is developed to cluster the data based on degree of criticality of the cancer patients and further classify it as benign or malignant utilizing probability of malignancy of the clusters along with assignment of grades of cancer. Firstly, after implementing data pre-processing step, significant features are selected using an integrated feature selection approach. An efficient weightage algorithm is proposed incorporating the knowledge of physicians and the benefits of regression analysis which thereby provides a novel approach for detection of breast cancer. A novel ensemble clustering and classification algorithm employing voting-based Weighted Interval Type-II Fuzzy Inference System and Staged Pegasos Quantum Support Vector Classifier is then developed basis the prioritization of clusters depicting the critical state of breast cancer. A grading approach is also proposed based on fuzzy linguistic multi-criteria decision making system. Finally, the research is validated on Wisconsin Breast Cancer dataset. The detailed implementation of the proposed integrated model is accomplished to establish its superiority over other existing models in the literature. [ABSTRACT FROM AUTHOR] |
| Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 163728120 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chatterjee%2C+Subhashis%22">Chatterjee, Subhashis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Das%2C+Ananya%22">Das, Ananya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ananyadas16@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Soft+Computing+-+A+Fusion+of+Foundations%2C+Methodologies+%26+Applications%22">Soft Computing - A Fusion of Foundations, Methodologies & Applications</searchLink>. Jun2023, Vol. 27 Issue 11, p7147-7178. 32p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+diagnosis%22">Cancer diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Early+detection+of+cancer%22">Early detection of cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+decision+making%22">Fuzzy decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+systems%22">Fuzzy systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Breast cancer is a life-threatening and consequential disease due to its invasive and proliferative trait, predominantly found in women. Early detection of the cancer is a significant contributor to improved mortality and hence is an area of keen focus for ongoing researches. However, developing a technique to diagnose the severity of the patients at an early stage is a challenging task. Manual diagnostic techniques are time-consuming and result in inaccurate diagnosis of breast cancer. Prompted by these facts, a quantum optimized rule-base generated automated framework is developed to cluster the data based on degree of criticality of the cancer patients and further classify it as benign or malignant utilizing probability of malignancy of the clusters along with assignment of grades of cancer. Firstly, after implementing data pre-processing step, significant features are selected using an integrated feature selection approach. An efficient weightage algorithm is proposed incorporating the knowledge of physicians and the benefits of regression analysis which thereby provides a novel approach for detection of breast cancer. A novel ensemble clustering and classification algorithm employing voting-based Weighted Interval Type-II Fuzzy Inference System and Staged Pegasos Quantum Support Vector Classifier is then developed basis the prioritization of clusters depicting the critical state of breast cancer. A grading approach is also proposed based on fuzzy linguistic multi-criteria decision making system. Finally, the research is validated on Wisconsin Breast Cancer dataset. The detailed implementation of the proposed integrated model is accomplished to establish its superiority over other existing models in the literature. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00500-023-07939-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 7147 Subjects: – SubjectFull: Multiple criteria decision making Type: general – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Cancer diagnosis Type: general – SubjectFull: Decision making Type: general – SubjectFull: Fuzzy logic Type: general – SubjectFull: Early detection of cancer Type: general – SubjectFull: Fuzzy decision making Type: general – SubjectFull: Fuzzy systems Type: general Titles: – TitleFull: An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chatterjee, Subhashis – PersonEntity: Name: NameFull: Das, Ananya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 14327643 Numbering: – Type: volume Value: 27 – Type: issue Value: 11 Titles: – TitleFull: Soft Computing - A Fusion of Foundations, Methodologies & Applications Type: main |
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