The Use of Background Knowledge in Decision Tree Induction
Machine Learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pessimistic decision tree pruning based Continuous-time
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Protocol Analysis in Intrusion Detection Using Decision Tree
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Nabs: A System for Detecting Resource Abuses via Characterization of Flow Content Type
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
CoCoST: A Computational Cost Efficient Classifier
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A hybrid SVM based decision tree
Pattern Recognition
Two case studies in cost-sensitive concept acquisition
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
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The standard prediction process of SVM requires acquisition of all the feature values for every instance. In practice, however, a cost is associated with the mere act of acquisition of a feature, e.g. CPU time needed to compute the feature out of raw data, the dollar amount spent for gleaning more information, or the patient wellness sacrificed by an invasive medical test, etc. In such applications, a budget constrains the classification process from using all of the features. We present, Ace-Cost, a novel classification method that reduces the expected test cost of SVM without compromising from the classification accuracy. Our algorithm uses a cost efficient decision tree to partition the feature space for obtaining coarse decision boundaries, and local SVM classifiers at the leaves of the tree to refine them. The resulting classifiers are also effective in scenarios where several features share overlapping acquisition procedures, hence the cost of acquiring them as a group is less than the sum of the individual acquisition costs. Our experiments on the standard UCI datasets, a network flow detection application, as well as on synthetic datasets show that, the proposed approach achieves classification accuracy of SVM while reducing the test cost by 40%-80%.