This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two ...This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.展开更多
文摘This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.
