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Classification is one of the most important tasks in machine learning. The ensemble classifier which consists of a number of basic classifiers is an efficient classification technique and has shown its effectiveness in many applications. The diversity and strength of the basic ones are two main elements which influence the performance of the ensemble classifier. Since different classification methods...
Usage of recognition systems has found many applications in almost all fields. Generally in design of multiple classifier systems, the more diverse the results of the classifiers, the more appropriate the aggregated result. While most of classification algorithms have obtained a good performance for specific problems they have not enough robustness for other problems. Combination of multiple classifiers...
Selective ensemble is effective for improve the classification performance through taking full advantage of the diversity and supplement between base classifiers. A BPSO (binary particle swarm optimization) based selective SVM ensemble approach is proposed to ensure the diversity and supplement among base classifiers in the training phase and high performance in the selection phase. Firstly, bootstrap...
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