论文标题
神经贪婪追求特征选择
Neural Greedy Pursuit for Feature Selection
论文作者
论文摘要
我们提出了一种贪婪算法，以在$ p $输入功能中为非线性预测问题选择$ n $重要功能。在迭代损失最小化过程中，依次选择该功能。我们将神经网络用作算法中的预测因子来计算损失，因此我们将我们的方法称为神经贪婪追求（NGP）。 NGP在选择$ n \ ll p $时可以有效地选择$ n $功能，并且在顺序选择过程之后，它在降序订单中提供了特征重要的概念。我们在实验上表明，NGP比几种特征选择方法（例如Deeplift和Drop-One-One-One损失）提供了更好的性能。此外，我们通过实验表明了一种相变行为，在训练数据大小超过阈值时，可以完美选择所有$ n $功能，而无需误报。
We propose a greedy algorithm to select $N$ important features among $P$ input features for a non-linear prediction problem. The features are selected one by one sequentially, in an iterative loss minimization procedure. We use neural networks as predictors in the algorithm to compute the loss and hence, we refer to our method as neural greedy pursuit (NGP). NGP is efficient in selecting $N$ features when $N \ll P$, and it provides a notion of feature importance in a descending order following the sequential selection procedure. We experimentally show that NGP provides better performance than several feature selection methods such as DeepLIFT and Drop-one-out loss. In addition, we experimentally show a phase transition behavior in which perfect selection of all $N$ features without false positives is possible when the training data size exceeds a threshold.
