Robust feature selection based on regularized brownboost loss

Feature selection is an important preprocessing step in machine learning and pattern recognition. It is also a data mining task in some real-world applications. Feature quality evaluation is a key issue when designing an algorithm for feature selection. The classification margin has been used widely to evaluate feature quality in recent years. In this study, we introduce a robust loss function, called Brownboost loss, which computes the feature quality and selects the optimal feature subsets to enhance robustness. We compute the classification loss in a feature space with hypothesis-margin and minimize the loss by optimizing the weights of features. An algorithm is developed based on gradient descent using L₂-norm reg-ularization techniques. The proposed algorithm is tested using UCI datasets and gene expression datasets, respectively. The experimental results show that the proposed algorithm is effective in improving the classification robustness.