TY - GEN
T1 - Feature value acquisition in testing
T2 - 23rd International Conference on Machine Learning, ICML 2006
AU - Sheng, Victor S.
AU - Ling, Charles X.
PY - 2006
Y1 - 2006
N2 - In medical diagnosis, doctors often have to order sets of medical tests in sequence in order to make an accurate diagnosis of patient diseases. While doing so they have to make a trade-off between the cost of the tests and possible misdiagnosis. In this paper, we use cost-sensitive learning to model this process. We assume that test examples (new patients) may contain missing values, and their actual values can be acquired at cost (similar to doing medical tests) in order to reduce misclassification errors (misdiagnosis). We propose a novel Sequential Batch Test algorithm that can acquire sets of attribute values in sequence, similar to sets of medical tests ordered by doctors in sequence. The goal of our algorithm is to minimize the total cost (i.e., the trade-off) of acquiring attribute values and misclassifications. We demonstrate the effectiveness of our algorithm, and show that it outperforms previous methods significantly. Our algorithm can be readily applied in real-world diagnosis tasks. A case study on the heart disease is given in the paper.
AB - In medical diagnosis, doctors often have to order sets of medical tests in sequence in order to make an accurate diagnosis of patient diseases. While doing so they have to make a trade-off between the cost of the tests and possible misdiagnosis. In this paper, we use cost-sensitive learning to model this process. We assume that test examples (new patients) may contain missing values, and their actual values can be acquired at cost (similar to doing medical tests) in order to reduce misclassification errors (misdiagnosis). We propose a novel Sequential Batch Test algorithm that can acquire sets of attribute values in sequence, similar to sets of medical tests ordered by doctors in sequence. The goal of our algorithm is to minimize the total cost (i.e., the trade-off) of acquiring attribute values and misclassifications. We demonstrate the effectiveness of our algorithm, and show that it outperforms previous methods significantly. Our algorithm can be readily applied in real-world diagnosis tasks. A case study on the heart disease is given in the paper.
UR - http://www.scopus.com/inward/record.url?scp=34250776037&partnerID=8YFLogxK
U2 - 10.1145/1143844.1143946
DO - 10.1145/1143844.1143946
M3 - Conference contribution
AN - SCOPUS:34250776037
SN - 1595933832
SN - 9781595933836
T3 - ACM International Conference Proceeding Series
SP - 809
EP - 816
BT - ACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006
Y2 - 25 June 2006 through 29 June 2006
ER -