Feature value acquisition in testing

Abstract

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.

Qualitative test-cost sensitive classification

“…Another possibility is to also include the other types of cost (e.g., delay cost (Sheng and Ling, 2006) and computational cost (Demir and Alpaydin, 2005)) into the problem formulation.…”

“…Sheng and Ling (2006) and Yang et al (2006) compute the posterior probability of a feature taking a particular value by using the Bayes' rule where likelihoods and priors are estimated by maximum likelihood estimation. Zhang and Ji (2006) compute posteriors using dynamic Bayesian networks.…”

“…These studies define their splitting criterion as a function of both the information gain of a feature and its extraction cost (Nunez, 1991;Tan, 1993). Alternatively, they use the sum of misclassification and test costs as a splitting criterion (Sheng and Ling, 2006;Yang et al, 2006). These studies use a greedy approach to construct their decision trees.…”

Cost‐sensitive classification with time constraint on incomplete data

“…Certain cost-sensitive learning algorithms consider the waiting time for test results as a type of cost [40]. Sheng and Ling [37] propose a sequential batch test for disease prediction, minimizing attribute acquisition, delay, and misclassification costs. Zhang [51] introduces test time as a waiting cost in cost-time sensitive classification, addressing missing values with sequential and batch test strategies.…”

“…Table 2 specifies the misclassification costs. We adopt the settings of the test cost and test time used in Turney [39], Sheng and Ling [37], and Chen et al [5]. The test cost and test time for the variables in the Heart Disease data appear in Table 3.…”