A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.
A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.
复制标题
一种基于增量半监督支持向量机的新型分类算法
DOI：
10.1371/journal.pone.0135709
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Hussain A
中科院分区：
文献类型：
--
作者：
Gao F;Mei J;Sun J;Wang J;Yang E;Hussain A
当前计算智能技术面临的一个主要挑战是如何在不断变化的环境中学习新概念。传统的学习方案不能充分解决这个问题，由于缺乏动态的数据选择机制。受人类学习过程的启发，提出了一种基于增量式半监督支持向量机（SVM）的分类算法。通过对样本预测置信度和变化环境下数据分布的分析，设计了“软启动”方法、数据选择机制和数据清洗机制，完成了增量式半监督学习系统的构建。值得注意的是，我们提出的算法巧妙的设计过程，有效地降低了计算复杂度。此外，对于学习过程中可能出现的一些新的标记样本，也进行了详细的分析。实验结果表明，该算法不依赖于样本分布模型，引入错误半标记样本的概率极低，能够有效利用未标记样本丰富分类器的知识体系，提高分类器的准确率。此外，我们的方法还具有出色的泛化性能和克服在不断变化的环境中的概念漂移的能力。
For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.