FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
复制标题
DOI：
10.1109/tfuzz.2010.2042721
复制
发表时间：
2010-06-01
影响因子：
11.9
通讯作者：
Palade, Vasile
中科院分区：
文献类型：
--
作者：
Batuwita, Rukshan;Palade, Vasile
支持向量机（svm）是一种流行的机器学习技术，它可以有效地处理平衡数据集。然而，当涉及到不平衡的数据集时，支持向量机产生次优分类模型。另一方面，支持向量机算法对数据集中存在的异常值和噪声敏感。因此，尽管现有的类不平衡学习（class imbalance learning， CIL）方法可以降低svm对类不平衡的敏感性，但仍然存在异常值和噪声的问题。模糊支持向量机（fsvm）是支持向量机算法的一种变体，用于处理异常值和噪声问题。在fsvm中，训练样例根据其重要性被赋予不同的模糊隶属度值，这些隶属度值被纳入SVM学习算法中，使其对异常值和噪声的敏感性降低。然而，与普通的SVM算法一样，fsvm也存在类不平衡的问题。本文提出了一种改进模糊支持向量机的方法（称为FSVM-CIL），该方法可用于处理存在异常值和噪声的类不平衡问题。我们在10个真实世界的不平衡数据集上全面评估了所提出的FSVM-CIL方法，并将其与现有的5种可用于正常SVM训练的CIL方法进行了性能比较。综合以上结果，我们可以得出结论，所提出的FSVM-CIL方法是一种非常有效的CIL方法，特别是在数据集中存在异常值和噪声的情况下。
Support vector machines (SVMs) is a popular machine learning technique, which works effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs produce suboptimal classification models. On the other hand, the SVM algorithm is sensitive to outliers and noise present in the datasets. Therefore, although the existing class imbalance learning (CIL) methods can make SVMs less sensitive to class imbalance, they can still suffer from the problem of outliers and noise. Fuzzy SVMs (FSVMs) is a variant of the SVM algorithm, which has been proposed to handle the problem of outliers and noise. In FSVMs, training examples are assigned different fuzzy-membership values based on their importance, and these membership values are incorporated into the SVM learning algorithm to make it less sensitive to outliers and noise. However, like the normal SVM algorithm, FSVMs can also suffer from the problem of class imbalance. In this paper, we present a method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise. We thoroughly evaluated the proposed FSVM-CIL method on ten real-world imbalanced datasets and compared its performance with five existing CIL methods, which are available for normal SVM training. Based on the overall results, we can conclude that the proposed FSVM-CIL method is a very effective method for CIL, especially in the presence of outliers and noise in datasets.