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Found 5 papers, 0 papers with code
Determination of class-specific variables in nonparametric multiple-class classification
In this paper, we propose a probability-based nonparametric multiple-class classification method, and integrate it with the ability of identifying high impact variables for individual class such that we can have more information about its classification rule and the character of each class as well.
Active learning for binary classification with variable selection
For a classification problem, this means that the essential label information may not be readily obtainable, in the data set in hands, and an extra labeling procedure is required such that we can have enough label information to be used for constructing a classification model.
Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation
Variable selection is a common way to increase the ability of model interpretation and is popularly used with some parametric classification models.
Distributed sequential method for analyzing massive data
To analyse a very large data set containing lengthy variables, we adopt a sequential estimation idea and propose a parallel divide-and-conquer method.
Greedy Active Learning Algorithm for Logistic Regression Models
Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model, comparing with that of the classifier trained with all variables and a full data set.
