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In many real-world applications, decision trees that take account of the cost of acquiring attributes for decision making have been the research focuses. The decision-making process must learn which sequence to perform, and how to build an inexpensive and reliable inductive learning model to accomplish its task. Many previous works in the area of test-cost sensitive decision tree learning have successfully...
Some existing test-cost sensitive learning algorithms are about balancing act of the misclassification cost and the total test cost, and the others focus on the balance between the classification accuracy and the total test cost. By far, however, few works reduce the total test cost, yet at the same time maintain the high classification accuracy. In order to achieve this goal, this paper modifies...
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