Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Classification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost
MetaCost: a general method for making classifiers cost-sensitive
Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?
Example-dependent cost-sensitive decision trees
Feature-cost sensitive learning with submodular trees of classifiers
Multi-class cost-sensitive boosting with p-norm loss functions
A survey of cost-sensitive decision tree induction algorithms
Optimizing F-Measures by Cost-Sensitive Classification
Cost-sensitive boosting for classification of imbalanced data
Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier Hayder
Cost-Sensitive Learning and the Class Imbalance Problem
Cost-sensitive learning by cost-proportionate example weighting
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
Cost-sensitive learning for large-scale hierarchical classification
Cost curves: An improved method for visualizing classifier performance
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
Cost-sensitive learning methods for imbalanced data
transductive based cost - sensitive multi - label classification ms .
Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams
Generalized k-Labelsets Ensemble for Multi-Label and Cost-Sensitive Classification
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
Potassium in hypertension and cardiovascular disease.
Exploring the use of shift register lookup tables for Keccak implementations on Xilinx FPGAs
Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
The Foundations of Cost-Sensitive Learning