A model of decision-making with sequential information-acquisition (part 1)
Decision Support Systems
A model of decision-making with sequential information-acquisition (part 2)
Decision Support Systems
Explicitly biased generalization
Computational Intelligence
The Use of Background Knowledge in Decision Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on 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
The cost-minimizing inverse classification problem: a genetic algorithm approach
Decision Support Systems
Sequential Decision Models for Expert System Optimization
IEEE Transactions on Knowledge and Data Engineering
Goal-Directed Classification Using Linear Machine Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
Management Science
INFORMS Journal on Computing
Mean-Risk Trade-Offs in Inductive Expert Systems
Information Systems Research
Journal of Artificial Intelligence Research
A threshold varying bisection method for cost sensitive learning in neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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We address a problem of classification with information acquisition cost constraint (CIACC). The objective of the CIACC problem is to develop a classification function that maximizes correct classifications under the user defined information acquisition cost constraint. We propose hybrid simulated annealing and neural network (SA-ANN), and tabu search and neural network (TS-ANN) procedures to solve the CIACC problem. Using simulated and a real-world data set from medical domain, we show that the proposed hybrid procedures solve the CIACC problem. The results of our experiments indicate that the performance of hybrid approaches is sensitive to the data distribution, and memory-based hybrid tabu search approaches may perform as good as or better than probabilistic hybrid simulated annealing approach.