Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results toward the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample’s neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM, and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.

中文翻译：

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不平衡数据的基于实例的熵模糊支持向量机

不平衡分类一直是机器学习的主要挑战，因为许多标准分类器主要关注平衡数据集，并且倾向于偏向多数类。我们修改了熵模糊支持向量机（EFSVM），并引入了基于实例的熵模糊支持向量机（IEFSVM）。EFSVM和IEFSVM都使用k个最近邻居的熵信息来确定每个样本的模糊隶属度，从而优先考虑每个样本的重要性。当增加邻居的大小k时，IEFSVM考虑每个样本的熵模式的多样性，而EFSVM对所有样本使用邻居的固定大小的单个熵信息。通过改变k，我们可以在确定模糊值隶属度时反映出样本邻居从近到远的成分变化。对35个公共和12个现实世界不平衡数据集进行了数值实验，以验证IEFSVM，并使用接收器工作特性曲线（AUC）下的面积与其他SVM和机器学习方法进行比较。IEFSVM对于具有高不平衡比的数据集显示更高的AUC值，这表明IEFSVM在处理类不平衡问题方面非常有效。