Given a set of observations, feature acquisition is about finding the subset
of unobserved features which would enhance accuracy. Such problems have been
explored in a sequential setting in prior work. Here, the model receives
feedback from every new feature acquired and chooses to explore more features
or to predict. However, sequential acquisition is not feasible in some settings
where time is of the essence. We consider the problem of feature acquisition in
batch, where the subset of features to be queried in batch is chosen based on
the currently observed features, and then acquired as a batch, followed by
prediction. We solve this problem using several technical innovations. First,
we use a feature generator to draw a subset of the synthetic features for some
examples, which reduces the cost of oracle queries. Second, to make the feature
acquisition problem tractable for the large heterogeneous observed features, we
partition the data into buckets, by borrowing tools from locality sensitive
hashing and then train a mixture of experts model. Third, we design a tractable
lower bound of the original objective. We use a greedy algorithm combined with
model training to solve the underlying problem. Experiments with four datasets
show that our approach outperforms these methods in terms of trade-off between
accuracy and feature acquisition cost.

在批量特征获取问题中，我们通过使用特征生成器、数据分区和贪心算法结合模型训练等技术创新，以在准确性和特征获取成本之间取得最佳平衡，成功解决了特征获取问题。

生成器辅助批处理中的专家混合特征提取

Generator Assisted Mixture of Experts For Feature Acquisition in Batch

Feature missing is a serious problem in many applications, which may lead to
low quality of training data and further significantly degrade the learning
performance. While feature acquisition usually involves special devices or
complex process, it is expensive to acquire all feature values for the whole
dataset. On the other hand, features may be correlated with each other, and
some values may be recovered from the others. It is thus important to decide
which features are most informative for recovering the other features as well
as improving the learning performance. In this paper, we try to train an
effective classification model with least acquisition cost by jointly
performing active feature querying and supervised matrix completion. When
completing the feature matrix, a novel target function is proposed to
simultaneously minimize the reconstruction error on observed entries and the
supervised loss on training data. When querying the feature value, the most
uncertain entry is actively selected based on the variance of previous
iterations. In addition, a bi-objective optimization method is presented for
cost-aware active selection when features bear different acquisition costs. The
effectiveness of the proposed approach is well validated by both theoretical
analysis and experimental study.

本文提出了一种通过联合执行主动特征查询和监督矩阵完成来训练有效分类模型的方法，以最小化获取成本，并同时降低误差和监督损失。此外，还提出了一种成本感知的活动选择双目标优化方法来选择不同获取成本的特征。该方法已通过理论分析和实验研究得到了有效验证。

具监督矩阵补全的主动特征获取

Active Feature Acquisition with Supervised Matrix Completion

We seek decision rules for prediction-time cost reduction, where complete
data is available for training, but during prediction-time, each feature can
only be acquired for an additional cost. We propose a novel random forest
algorithm to minimize prediction error for a user-specified {\it average}
feature acquisition budget. While random forests yield strong generalization
performance, they do not explicitly account for feature costs and furthermore
require low correlation among trees, which amplifies costs. Our random forest
grows trees with low acquisition cost and high strength based on greedy minimax
cost-weighted-impurity splits. Theoretically, we establish near-optimal
acquisition cost guarantees for our algorithm. Empirically, on a number of
benchmark datasets we demonstrate superior accuracy-cost curves against
state-of-the-art prediction-time algorithms.

该研究提出一种新颖的随机森林算法，通过贪心极小最大代价加权不纯度分割，使得生成具有低成本和高强度的树，来实现在特征收购预算下，如何在保证高准确性的同时降低预测时间成本。理论分析表明算法的收购成本接近最优，实验结果在多个基准数据集上优于基准算法。