We propose a new ensemble classification algorithm, named Super Random Subspace Ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the Random Subspace Ensemble algorithm (RaSE). The RaSE method …
A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to …
Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the predictors according to their individual contributions to the …
We propose a new model-free ensemble classification framework, Random Subspace Ensemble (RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners, where each weak learner is a base classifier trained in a subspace …
References:
Tian, Y. and Feng, Y. (2021). RaSE: Random Subspace Ensemble Classification. Journal of Machine Learning Research.
Tian, Y. and Feng, Y. (2021). RaSE: A variable screening framework via random subspace ensembles. Journal of the American Statistical Association.
Zhu, J. and Feng, Y. (2021). Super RaSE: Super Random Subspace Ensemble Classification. Journal of Risk and Financial Management.
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In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it …
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