基于区域标记法的代价敏感支持向量机在股票预测中的研究
A Study of Cost-Sensitive SVM based on Region Labeling Method in Stock Prediction

本文针对传统股票预测中单点标记法的缺陷，提出了区域标记法，区域标记法可以为训练分类器提供更多有用信息，在一定程度上减轻了类别不平衡的问题，也更能满足实际任务的需求.同时，本文构建了一个RCS-Trader模型，该模型使用了代价敏感的支持向量机和F_S度量进行优化，相比于传统股票预测方法，RCS-Trader模型的效果更好，投资回报率更高.
In this paper, the region labeling method is proposed for the shortcomings of single point labeling method in traditional stock forecasting. The region labeling method can provide more useful information for training classifier and alleviate the problem of class imbalance to a certain extent, which is also more suitable for practical needs. At the same time, this paper constructs an RCS-Trader model, which uses cost-sensitive support vector machines and F_S measure to optimize. Compared with traditional stock predicting methods, RCS-Trader model works better and has higher return rate of investment