摘要
Support vector machines （SVMs） are combined with the artificial immune network （aiNet）, thus forming a new hybrid ai-SVM algorithm. The algorithm is used to reduce the number of samples and the training time of SVM on large datasets, aiNet is an artificial immune system （AIS） inspired method to perform the automatic data compression, extract the relevant information and retain the topology of the original sample distribution. The output of aiNet is a set of antibodies for representing the input dataset in a simplified way. Then the SVM model is built in the compressed antibody network instead of the original input data. Experimental results show that the ai-SVM algorithm is effective to reduce the computing time and simplify the SVM model, and the accuracy is not decreased.
为了减少大规模数据的支持向量机的样本训练时间,提出了人工免疫(aiNet)和支持向量机(SVM)相结合的算法(ai-SVM)。aiNet能在进行样本压缩的同时抽取原始数据的相关信息并保持原始数据的样本分布。压缩后的样本组成了抗体网络,并在此抗体网络上构建了支持向量机模型。最后结合实际数据样本对ai-SVM算法进行了验证。结果表明,ai-SVM算法可大大减小训练样本集和训练代价,且不降低精度。
