Test-Cost Sensitive Classification Based on Conditioned Loss Functions

We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy.

[1]  Qiang Yang,et al.  Test-cost sensitive classification on data with missing values , 2006, IEEE Transactions on Knowledge and Data Engineering.

[2]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[3]  Neil D. Lawrence,et al.  Missing Data in Kernel PCA , 2006, ECML.

[4]  Dan Roth,et al.  Learning cost-sensitive active classifiers , 2002, Artif. Intell..

[5]  Peter D. Turney.  Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification , 2002, ArXiv.

[6]  Peter D. Turney.  Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..

[7]  Cigdem Demir,et al.  Cost-conscious classifier ensembles , 2005, Pattern Recognit. Lett..

[8]  Steven W. Norton.  Generating Better Decision Trees , 1989, IJCAI.

[9]  David G. Stork,et al.  Pattern Classification , 1973 .

[10]  Jason V. Davis,et al.  Cost-Sensitive Decision Tree Learning for Forensic Classification , 2006, ECML.

[11]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[12]  Peter D. Turney.  Types of Cost in Inductive Concept Learning , 2002, ArXiv.

[13]  Lawrence Carin,et al.  Cost-sensitive feature acquisition and classification , 2007, Pattern Recognit..

[14]  Y. Zhang,et al.  Active and dynamic information fusion for multisensor systems with dynamic bayesian networks , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Ming Tan,et al.  Cost-sensitive learning of classification knowledge and its applications in robotics , 2004, Machine Learning.

[16]  Thomas G. Dietterich,et al.  Pruning Improves Heuristic Search for Cost-Sensitive Learning , 2002, ICML.