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
C4.5: programs for machine learning
C4.5: programs for 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
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Efficient GA Based Techniques for Classification
Applied Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
On Feature Selection with Measurement Cost and Grouped Features
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
An introduction to variable and feature selection
The Journal of Machine Learning Research
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Attribute Measurement Policies for Time and Cost Sensitive Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Building a cost-constrained decision tree with multiple condition attributes
Information Sciences: an International Journal
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
Cost-sensitive test strategies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Artificial Intelligence Review
Information Sciences: an International Journal
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
Confidence intervals for dependent data: Equating non-overlap with statistical significance
Computational Statistics & Data Analysis
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Hybrid cost-sensitive decision tree
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Simple test strategies for cost-sensitive decision trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
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
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Feature selection is an essential process for machine learning tasks since it improves generalization capabilities, and reduces run-time and a model's complexity. In many applications, the cost of collecting the features must be taken into account. To cope with the cost problem, we developed a new cost-sensitive fitness function based on histogram comparison. This function is integrated with a genetic search method to form a new feature selection algorithm termed CASH (cost-sensitive attribute selection algorithm using histograms). The CASH algorithm takes into account feature collection costs as well as feature grouping and misclassification costs. Our experiments in various domains demonstrated the superiority of CASH over several other cost-sensitive genetic algorithms.