Maximizing the predictive value of production rules
Artificial Intelligence
The Use of Background Knowledge in Decision Tree Induction
Machine Learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Learning cost-sensitive active classifiers
Artificial Intelligence
Learning cost-sensitive diagnostic policies from data
Learning cost-sensitive diagnostic policies from data
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Test-Cost Sensitive Classification on Data with Missing Values
IEEE Transactions on Knowledge and Data Engineering
Cost-conscious classifier ensembles
Pattern Recognition Letters
Feature value acquisition in testing: a sequential batch test algorithm
ICML '06 Proceedings of the 23rd international conference on Machine learning
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Cost-sensitive feature acquisition and classification
Pattern Recognition
Semi-parametric optimization for missing data imputation
Applied Intelligence
Cost-sensitive test strategies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
GBKII: an imputation method for missing values
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Cost-time sensitive decision tree with missing values
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
Any-cost discovery: learning optimal classification rules
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Cost-Sensitive decision trees with multiple cost scales
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Feature selection with test cost constraint
International Journal of Approximate Reasoning
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Extant multiple-cost-sensitive learning algorithms are usually designed for dealing with misclassification cost (MC) and test cost (TC) together. This paper outlines a new learning algorithm, called cost-time sensitive classification, designed for minimizing tangible costs (which includes TC and waiting cost (WC)) as well as maximizing the decrease of the intangible costs (also called MC). The proposed algorithm induces decision trees from training datasets with missing data, in which the costs are measured in different units. Firstly, a split criterion is proposed for building cost-time sensitive decision trees, aiming at possibly reducing the intangible cost. Then a hybrid test strategy, which can handle missing values in test datasets, is designed for combining the sequential test with the batch test strategy. To evaluate the efficiency of the proposed method, extensive experiments were conducted on the UCI datasets at different missing rates. The experimental results show that the proposed algorithm achieves better than the existing ones in terms of reducing the intangible costs when taking into account waiting costs.