In our method each classifier were graded according to their effectiveness of providing more accurate results. This approach first utilizes the best classifier.
In our method each classifier were graded according to their effectiveness of providing more accurate results. This approach first utilizes the best classifier.
In our method each classifier were graded according to their effectiveness of providing more accurate results. This approach first utilizes the best classifier.
This paper presents a framework for the analysis of similarity among abstract-level classifiers and proposes a methodology for the evaluation of combination ...
In the paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed ...
Missing: Novel | Show results with:Novel
Our model can significantly improve classification training time by combining a compact subset of relevant features without the loss of accuracy.
The specific aim of this proposal is to develop and evaluate a novel framework for the effective analysis and subsequent synthesis of systems involv the fusion ...
Missing: Efficient | Show results with:Efficient
The simulation results demonstrate that the proposed SVM multi-classification strategy can simultaneously improve the training efficiency and classification ...
Jan 30, 2024 · In this study, a fast and efficient multi-label classifier, named PredictEFC, was designed. To construct this classifier, a novel feature extraction scheme was ...
Sep 24, 2007 · A number of viable methods to design MCSs have been developed including bagging, adaboost, rotation forest, and random subspace. They have been ...