In the field of machine learning, collected data always have additional features which are always referred as privileged information. Privileged information learning is mainly used to help train the classifier in the training process, and predict the unseen example by the learned classifier. In this paper, we propose a new method named twin support vector machines with privileged information (TWSVM-PI). In the proposed method, we first introduce the privileged information into twin SVMs so as to construct a model for prediction, and then utilize the Lagrangian multiplier method to optimize the proposed objective function. Thus, we obtain two nonparallel classification hyperplanes by solving two smaller sized quadratic programming problems (QPPs), which can shorter the computational time and improve the accuracy of the prediction. Finally, we conduct extensive experiments to evaluate the performance of the proposed TWSVM-PI method. The results have shown that our proposed method can obtain a better performance compared with state-of-the-art methods.

在机器学习领域，收集的数据总是具有附加特征，这些特征总是被称为特权信息。特权信息学习主要用于在训练过程中帮助训练分类器，通过学习到的分类器预测看不见的例子。在本文中，我们提出了一种名为具有特权信息的孪生支持向量机（TWSVM-PI）的新方法。在所提出的方法中，我们首先将特权信息引入孪生SVM以构建预测模型，然后利用拉格朗日乘子法优化所提出的目标函数。因此，我们通过求解两个较小尺寸的超平面来获得两个不平行的分类超平面二次规划问题（QPPs），可以缩短计算时间并提高预测的准确性。最后，我们进行了大量实验来评估所提出的 TWSVM-PI 方法的性能。结果表明，与最先进的方法相比，我们提出的方法可以获得更好的性能。