딥러닝 기반 앙상블을 이용한 유방암 분류
Breast Cancer Classification using Deep Learning-based Ensemble
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조회
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다운로드
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인용
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참고문헌
키워드
초록
Objectives: We propose a deep learning-based ensemble for improving breast cancer classification and compare it with existing six models including deep neural network on two UCI data. Methods: We propose a deep learning-based stacking ensemble method. We first applied five classifications methods individually, which were k-nearest neighbor, decision trees, support vector machines, discriminant analysis, and logistic regression analysis and then adopt a deep learning to the predictions derived from these methods after using 5-fold cross validation technique. We compared the proposed deep learning-based ensemble method with these methods for two UCI data through classification accuracy, ROC curves and c-statistics. Results: Experimental results for two UCI data showed that the proposed deep learning-based ensemble outperformed single k-nearest neighbor, decision trees, support vector machines discriminant analysis, and logistic regression analysis as well as deep neural network in terms of various performance measures. Conclusions: We proposed deep learning-based ensemble for improving breast cancer classification. The deep learning-based ensemble outperformed existing single models for all applications in terms of various performance measures.
Abstract
Objectives: We propose a deep learning-based ensemble for improving breast cancer classification and compare it with existing six models including deep neural network on two UCI data. Methods: We propose a deep learning-based stacking ensemble method. We first applied five classifications methods individually, which were k-nearest neighbor, decision trees, support vector machines, discriminant analysis, and logistic regression analysis and then adopt a deep learning to the predictions derived from these methods after using 5-fold cross validation technique. We compared the proposed deep learning-based ensemble method with these methods for two UCI data through classification accuracy, ROC curves and c-statistics. Results: Experimental results for two UCI data showed that the proposed deep learning-based ensemble outperformed single k-nearest neighbor, decision trees, support vector machines discriminant analysis, and logistic regression analysis as well as deep neural network in terms of various performance measures. Conclusions: We proposed deep learning-based ensemble for improving breast cancer classification. The deep learning-based ensemble outperformed existing single models for all applications in terms of various performance measures.
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