Cost-sensitive Dynamic Feature Selection
Cost-sensitive Dynamic Feature Selection
复制标题
成本敏感的动态特征选择
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Jason Eisner
中科院分区：
文献类型：
--
作者：
He He;Hal Daum;Jason Eisner
提出了一种基于实例的测试时动态特征选择算法。我们的算法在给定先前选择的特征及其值的情况下依次选择特征。它停止选择过程，根据用户指定的准确性和成本权衡做出预测。我们将序列决策问题作为一个马尔可夫决策过程，并应用模仿学习技术。我们在一个简单的多类分类设置中共同解决了学习和推理的问题。在UCI数据集上的实验结果表明，与静态特征选择方法相比，我们的方法仅使用一小部分特征就可以达到相同或更高的精度。
We present an instance-specific test-time dynamic feature selection algorithm. Our algorithm sequentially chooses features given previously selected features and their values. It stops the selection process to make a prediction according to a user-specified accuracy-cost trade-off. We cast the sequential decision-making problem as a Markov Decision Process and apply imitation learning techniques. We address the problem of learning and inference jointly in a simple multiclass classification setting. Experimental results on UCI datasets show that our approach achieves the same or higher accuracy using only a small fraction of features than static feature selection methods.