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中文摘要：
本文提出一种自整定权值的融合方法．该方法使用混淆矩阵来衡量分类器性能，并根据分类器输出情况自适应地为各分类器赋予权值，可靠的决策结果获得较大的权值，从而提高决策模板的可信度．对易于被错误分类的样本，在利用其与决策模板的相似性信息的同时，结合它周围的训练样本信息做出判断．通过与DT方法在KDD’99入侵检测数据集和UCI数据库中的8个数据集上的实验对比，表明本文方法具有更好的分类性能．
英文摘要：
A fusion method with self-adjusting weights is proposed, which measures the classifier performance by the confusion matrix,and self-adaptively assigns weights to classifiers based on their outputs. Bigger weights are assigned to reliable outputs so that the decision templates are more credible. For a sample which is prone to be misclassified,besides the similarity between it and the decision templates, the information of the training samples around it are included to make a decision. Experiments were done on the KDD＇ 99 intrusion detection dataset and 8 datasets from the database UCI to compare the proposed method with the DT method. The experimental results show the presented method has a better classification oerformance.
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