Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
This addresses feature selection in semi-supervised learning for data analysis, but it is incremental as it builds on existing wrapper and pseudo-labeling techniques.
The paper tackles the problem of semi-supervised feature selection with imperfect pseudo-labels by proposing a wrapper approach that uses a genetic algorithm and a new multi-class C-bound to account for mislabeling errors, achieving effectiveness compared to state-of-the-art methods on various datasets.
In this paper, we propose a new wrapper feature selection approach with partially labeled training examples where unlabeled observations are pseudo-labeled using the predictions of an initial classifier trained on the labeled training set. The wrapper is composed of a genetic algorithm for proposing new feature subsets, and an evaluation measure for scoring the different feature subsets. The selection of feature subsets is done by assigning weights to characteristics and recursively eliminating those that are irrelevant. The selection criterion is based on a new multi-class $\mathcal{C}$-bound that explicitly takes into account the mislabeling errors induced by the pseudo-labeling mechanism, using a probabilistic error model. Empirical results on different data sets show the effectiveness of our framework compared to several state-of-the-art semi-supervised feature selection approaches.