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Found 4 papers, 1 papers with code
Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette
This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks.
Graph-based Extreme Feature Selection for Multi-class Classification Tasks
We aim to drastically reduce the number of selected features, in order to create a sketch of the original data that codes valuable information for the classification task.
Adaptive Learning for the Resource-Constrained Classification Problem
Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufacturing settings under a limited inspection budget.
Adaptive Cost-Sensitive Learning in Neural Networks for Misclassification Cost Problems
We design a new adaptive learning algorithm for misclassification cost problems that attempt to reduce the cost of misclassified instances derived from the consequences of various errors.
