A Weighted Voting Classifier Based on Differential Evolution

المؤلفون المشاركون

Cai, Jing
Zhang, Hongrui
Yang, Binbin
Zhang, Yong

المصدر

Abstract and Applied Analysis

العدد

المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-6، 6ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2014-05-22

دولة النشر

مصر

عدد الصفحات

6

التخصصات الرئيسية

الرياضيات

الملخص EN

Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier.

Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution.

After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule.

Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Zhang, Yong& Zhang, Hongrui& Cai, Jing& Yang, Binbin. 2014. A Weighted Voting Classifier Based on Differential Evolution. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1033720

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Zhang, Yong…[et al.]. A Weighted Voting Classifier Based on Differential Evolution. Abstract and Applied Analysis No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1033720

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Zhang, Yong& Zhang, Hongrui& Cai, Jing& Yang, Binbin. A Weighted Voting Classifier Based on Differential Evolution. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1033720

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1033720