Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Cost-sensitive concept learning of sensor use in approach and recognition
Proceedings of the sixth international workshop on Machine learning
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
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Boosting Trees for Cost-Sensitive Classifications
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
Semi-parametric optimization for missing data imputation
Applied Intelligence
Proceedings of the 24th international conference on Machine learning
A Strategy for Attributes Selection in Cost-Sensitive Decision Trees Induction
CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
Cost-Sensitive Decision Trees with Pre-pruning
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Cost-sensitive test strategies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Generating better decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Cost-sensitive classification with respect to waiting cost
Knowledge-Based Systems
Journal of Systems and Software
Missing data imputation by utilizing information within incomplete instances
Journal of Systems and Software
Cost-Sensitive decision tree learning for forensic classification
ECML'06 Proceedings of the 17th European conference on Machine Learning
Any-cost discovery: learning optimal classification rules
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Dynamic test-sensitive decision trees with multiple cost scales
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Cost-Sensitive decision trees with multiple cost scales
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Cost-sensitive decision trees applied to medical data
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Nearest neighbor selection for iteratively kNN imputation
Journal of Systems and Software
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There are nine major types of cost involved in cost-sensitive learning that are with heterogeneous units in general, referred to heterogeneous costs. Extant cost-sensitive learning (CSL) algorithms are based on the assumption that all involved costs can be transformed into a unified unit, called as homogeneous assumption of costs. While it is a challenge to construct many suitable transformation functions for the costs with diverse units, this paper designs a heterogeneous-cost sensitive learning (HCSL) algorithm to make split attribute selection more effective. This paper first proposes an efficient method of reducing the heterogeneity caused by both cost mechanisms and attribute information. And then, all heterogeneous costs with attribution information together are incorporated into the process of split attribute selection, called as HCAI-based split attribute selection. Third, the over-fitting is tackled by designing a simple and effective smoothing strategy, so as to build cost-sensitive decision tree classifiers with the HCSL algorithm. Experiments are conducted to evaluate the proposed HCSL algorithm on six UCI datasets. Experimental results show that the proposed approach outperforms existing methods for handling the heterogeneity caused by cost mechanisms and attribute information.