This paper introduces a novel approach to active feature acquisition for
classification, which is the task of sequentially selecting the most
informative subset of features to achieve optimal prediction performance during
testing while minimizing cost. The proposed approach involves a new lazy model
that is significantly faster and more efficient compared to existing methods,
while still producing comparable accuracy results. During the test phase, the
proposed approach utilizes Fisher scores for feature ranking to identify the
most important feature at each step. In the next step the training dataset is
filtered based on the observed value of the selected feature and then we
continue this process to reach to acceptable accuracy or limit of the budget
for feature acquisition. The performance of the proposed approach was evaluated
on synthetic and real datasets, including our new synthetic dataset, CUBE
dataset and also real dataset Forest. The experimental results demonstrate that
our approach achieves competitive accuracy results compared to existing
methods, while significantly outperforming them in terms of speed. The source
code of the algorithm is released at github with this link:
this https URL

本文提出了一种新的主动特征获取方法，使用 Lazy Model 和 Fisher scores 确定特征子集以在测试期间最大程度地提高预测性能同时最小化成本（即特征获取成本达到可接受的精度或预算限制），并在合成和实际数据集上进行了实验来证明其准确性和速度优势。