Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Novel ensemble classifier framework for improved classification of breast lesions.Ensemble generation algorithm using different types of breast lesion features.Ensemble selection mechanism to find an optimal subset of component classifiers.Impressive classification performance by comparing single classifier based methods.  presents a novel ensemble classifier framework for improved classification of mammographic lesions in Computer-aided Detection (CADe) and Diagnosis (CADx) systems. Compared to previously developed classification techniques in mammography, the main novelty of proposed method is twofold: (1) the "combined use" of different feature representations (of the same instance) and data resampling to generate more diverse and accurate base classifiers as ensemble members and (2) the incorporation of a novel "ensemble selection" mechanism to further maximize the overall classification performance. In addition, as opposed to conventional ensemble learning, our proposed ensemble framework has the advantage of working ll with both ak and strong classifiers, extensively used in mammography CADe and/or CADx systems. Extensive experiments have been performed using benchmark mammogram dataset to test the proposed method on two classification applications: (1) false-positive (FP) reduction using classification beten masses and normal tissues, and (2) diagnosis using classification beten malignant and benign masses. Results shod promising results that the proposed method (area under the ROC curve (AUC) of 0.932 and 0.878, each obtained for the aforementioned two classification applications, respectively) impressively outperforms (by an order of magnitude) the most commonly used single neural network (AUC?=?0.819 and AUC?=0.754) and support vector machine (AUC?=?0.849 and AUC?=?0.773) based classification approaches. In addition, the feasibility of our method has been successfully demonstrated by comparing other state-of-the-art ensemble classification techniques such as Gentle AdaBoost and Random Forest learning algorithms.
['Jae Young Choi', 'Dae Hoe Kim', 'Konstantinos N. Plataniotis', 'Yong Man Ro']
I am writing a research paper on a idea Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography
[{"from": "human", "value": "I am writing a research paper on a idea Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Novel ensemble classifier framework for improved classification of breast lesions.Ensemble generation algorithm using different types of breast lesion features.Ensemble selection mechanism to find an optimal subset of component classifiers.Impressive classification performance by comparing single classifier based methods.  presents a novel ensemble classifier framework for improved classification of mammographic lesions in Computer-aided Detection (CADe) and Diagnosis (CADx) systems. Compared to previously developed classification techniques in mammography, the main novelty of proposed method is twofold: (1) the \"combined use\" of different feature representations (of the same instance) and data resampling to generate more diverse and accurate base classifiers as ensemble members and (2) the incorporation of a novel \"ensemble selection\" mechanism to further maximize the overall classification performance. In addition, as opposed to conventional ensemble learning, our proposed ensemble framework has the advantage of working ll with both ak and strong classifiers, extensively used in mammography CADe and/or CADx systems. Extensive experiments have been performed using benchmark mammogram dataset to test the proposed method on two classification applications: (1) false-positive (FP) reduction using classification beten masses and normal tissues, and (2) diagnosis using classification beten malignant and benign masses. Results shod promising results that the proposed method (area under the ROC curve (AUC) of 0.932 and 0.878, each obtained for the aforementioned two classification applications, respectively) impressively outperforms (by an order of magnitude) the most commonly used single neural network (AUC?=?0.819 and AUC?=0.754) and support vector machine (AUC?=?0.849 and AUC?=?0.773) based classification approaches. In addition, the feasibility of our method has been successfully demonstrated by comparing other state-of-the-art ensemble classification techniques such as Gentle AdaBoost and Random Forest learning algorithms."}]