Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis
المؤلفون المشاركون
Gan, Dan
Shen, Jiang
Xu, Man
Liu, Na
Qi, Er-Shi
Gao, Bo
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-11-28
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers.
It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time.
Nowadays, numerous classification methods have been utilized for breast cancer diagnosis.
However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis.
To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous.
Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer.
In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models.
To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied.
The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods.
Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Na& Shen, Jiang& Xu, Man& Gan, Dan& Qi, Er-Shi& Gao, Bo. 2018. Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1207187
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Na…[et al.]. Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis. Mathematical Problems in Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1207187
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Na& Shen, Jiang& Xu, Man& Gan, Dan& Qi, Er-Shi& Gao, Bo. Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1207187
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1207187
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر