Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
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.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Dual Teaching: A Practical Semi-supervised Wrapper Method
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervise…
PPFS: Predictive Permutation Feature Selection
We propose Predictive Permutation Feature Selection (PPFS), a novel wrapper-based feature selection method based on the concept of Markov Blanket (MB). Unlike previous MB methods, PPFS is a universal feature selection te…
feature selectionSubspace Learning for Feature Selection via Rank Revealing QR Factorization: Unsupervised and Hybrid Approaches with Non-negative Matrix Factorization and Evolutionary Algorithm
The selection of most informative and discriminative features from high-dimensional data has been noticed as an important topic in machine learning and data engineering. Using matrix factorization-based techniques such a…
feature selectionNetwork Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection
One of the key challenges of machine learning (ML) based intrusion detection system (IDS) is the expensive computational complexity which is largely due to redundant, incomplete, and irrelevant features contain in the ID…
feature selectionIntrusion DetectionNetwork Intrusion DetectionMeta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction
Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the …
Bilevel OptimizationClick-Through Rate Predictionfeature selectionMeta-Learning+1