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中文摘要：
提出了一种新的模糊支持向量机模型——非平衡数据分类的支持向量机模型,通过改进惩罚函数,降低模型对于含有噪声点的非平衡样本数据的敏感性,并采用网格搜索算法来确定各个支持向量机模型中参数的优化取值.研究结果表明,非平衡数据分类的支持向量机模型对非平衡样本数据进行分类的效果优于其他方法,不仅总体判别精度较高,也提高了少数类样本的判别精度,取得了较好的改进效果.
英文摘要：
The paper proposes a new fuzzy SVM,called CI-FSVM（Class Imbalance Fuzzy Support Vector Machine）short for which is based on imbalanced datasets classification.By improving penalty functions,we reduce the sensitivity of the model for imbalanced datasets withoverlap＂.In addition,the parameters in SVM models are optimized by the grid-parameter-search algorithm.The results show that the CI-FSVM has a better effect in imbalanced datasets classification compared with other models.It not only has a higher overall accuracy,but also improves are judgment accuracy when dealing with the minority classifications.
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