面向不平衡数据的逻辑回归偏标记学习算法

偏标记学习是近几年提出的新机器学习框架,已有的逻辑回归偏标记算法尚不能解决数据不平衡问题.建立了一种可以解决数据不平衡的逻辑回归模型偏标记学习算法.基本思想是在多元逻辑回归模型中定义新的似然函数以达到处理不平衡数据的目的.算法先根据训练集中各个类别样本所占比例定义了一个新的似然函数,之后通过逼近和求导等数学手段推导得到了能够求解的光滑的逻辑回归偏标记学习模型.在UCI数据集和真实数据集上的仿真实验表明,所提算法在数据存在不平衡问题时提高了样本的平均分类精度.

Partial label learning algorithm for imbalanced data based on logistic regression

Partial label learning is a new machine learning framework proposed in recent years, but existing partial label learning algorithms based on logistic regression have not solved the problem of data imbalance. A partial label learning algorithm for data imbalance is presented based on logistic regression model. The basic idea is to define a new likelihood function in the multiple logistic regression models to deal with imbalanced data. Firstly, a new likelihood function is defined according to the proportion of each class sample in the training set; then, the smooth and logistic regression-based partial label learning model is derived through derivation and approximation method. Simulation experiments on UCI data sets and real world data sets show that the proposed algorithm improves the average classification accuracy of sample for data imbalance problem.