A Robust Cost-Sensitive Feature Selection Via Self-Paced Learning Regularization

Feature selection is a useful and important process, which has a widely use in high-dimensional data processing and artificial intelligence. Its goal is to select a relatively small and representative subset of data from the original data space so that can obtain a better learning performance. Many existing feature selection algorithms simply pursue high accuracy and ignore the cost of feature acquisition and error classification. In this paper, a novel cost-sensitive feature selection method via self-paced learning is proposed. The \(\sigma \)-norm is introduced to constrain the model and enhance its robustness. Then, we combine the self-paced learning framework. It can control the number of samples involved in training process, which can reduce the impact of noise points. The proposed method can obtain a better classification accuracy while maintaining the lowest total cost. It also has better interpretability and practicability than the traditional feature selection algorithm due to the consideration of the collection and misclassification cost among various features. Extensive experiments have been conducted on eight data sets with six comparison algorithms. The proposed method achieves a good performance.