为改善多分类器系统的分类性能，提出了基于广义粗集的集成特征选择方法。
For improving the performance of multiple classifier system, a novel method of ensemble feature selection is proposed based on generalized rough set.
多分类器系统能够在一定程度上弥补单个分类器的缺陷，因此它在模式识别中得到了广泛的应用。
Since multiple classifier systems can to some extent improve the performance of classification, the technique has been widely used in various fields of pattern recognition.
针对标准数据集在评估多分类器系统的组合方法时存在的不足，设计了一种新的分类器模拟算法。
Aiming at the deficiency of evaluating classifier combination methods with standard data sets, a new classifier simulation algorithm was proposed.
对UCI机器学习数据库的实验证明，相对于其它方法，EPD方法对多分类器系统性能的预测能力更强。
The experiments on UCI Machine Learning Repository prove that, compared to existing measures, EPD shows stronger ability in predicting the performance of multiple classifier systems.
为改进多分类器系统的性能，提出一个多分类器融合模型，该模型将和规则与多数投票作为特例纳入其体系中。
Aiming at improving the classification performance, a combination model of multiple classifier systems is presented, which takes the Sum rule and majority voting as its special cases.
分类器选择是一种设计多分类器系统的有效方法，从给定候选分类器集中挑选出一个子集，使得该子集集成性能最佳。
The goal of classifier selection is to select a subset of classifiers from a given set of candidate classifiers, to achieve the best combination performance.
组合多分类器可以看作是一种用于获得较高识别效果的混合系统。
Combining Multiple Classifiers can be viewed as a novel hybrid system to achieve high recognition accuracy for Text Independent Speaker Identification.
实验结果表明：利用多特征组合多分类器的方法可以提高“文本无关”说话人辨认系统的识别率和可靠性。
The experimental results have shown that Combining Multiple Classifiers with different features can result in satisfactory and significant improvement in recognition performance.
实验结果表明，该方法能够用可理解性好的模糊系统实现低错误率的多分类器融合。
The experimental results show that the proposed method can fuse multiple classifiers with low classification error rate based on comprehensible fuzzy systems.
多分类器集成系统是当前机器学习领域的一个研究热点。
Integration of multiple classifier machine learning system is currently a hot research field.
多分类器集成系统是当前机器学习领域的一个研究热点。
Integration of multiple classifier machine learning system is currently a hot research field.
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