Unlike traditional statistical methods, Conformal Prediction (CP) allows for
the determination of valid and accurate confidence levels associated with
individual predictions based only on exchangeability of the data. We here
introduce a new feature selection method that takes advantage of the CP
framework. Our proposal, named Conformal Recursive Feature Elimination (CRFE),
identifies and recursively removes features that increase the non-conformity of
a dataset. We also present an automatic stopping criterion for CRFE, as well as
a new index to measure consistency between subsets of features. CRFE selections
are compared to the classical Recursive Feature Elimination (RFE) method on
several multiclass datasets by using multiple partitions of the data. The
results show that CRFE clearly outperforms RFE in half of the datasets, while
achieving similar performance in the rest. The automatic stopping criterion
provides subsets of effective and non-redundant features without computing any
classification performance.

提出了基于置信度预测的递归特征消除方法，命名为 CRFE，该方法在多类别数据集上通过多次数据分区的比较，表现出优于传统 RFE 方法的性能，并提供有效和非冗余特征的子集而无需计算任何分类性能。