By analyzing the labeling error types, we slightly revise our classification scheme and propose six methods to improve the classification and labeling system, hoping to achieve even better agreement in the future.
In order to improve the classification accuracy of the system, event filtration and event categorization processes are applied.
This framework, which we call 'discriminant cluster selection', aims to improve the classification accuracy in decision-making systems by providing an alternative solution to the feature overlapping problem mentioned above.
The individual classifier has its own strengths and weaknesses; therefore, it is inspired to integrate multiple classifiers, that is, voting with the majority system, to improve the classification performance.
Hence, these two issues are addressed using a novel technique PNN-ABC based optimal feature selection and parameter optimization for improving the performance of the classification system.
Clarification of the etiology of borderline tumors and the pathogenesis of their associated implants is critical for improving pathological diagnosis, revising the classification system of ovarian neoplasms, and developing optimal, evidence-based clinical management algorithms.
Specifically, by lowering the threshold, more instances will be classified as positive, potentially improving the recall of the classification system.
