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The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy-based methods, use genetic algorithms, use anytime… (More)

The validation of data from sensors has be come an important issue in the operation and control of modern industrial plants. One ap proach is to use know ledge based techniques to detect inconsistencies in measured data. This article presents a probabilistic model for the detection of such inconsistencies. Based on probability propagation, this method is… (More)

An important advantages of using a formal method of developing software is that one can prove that development steps are correct with respect to their specification. Conducting proofs by hand, however, can be time consuming to the extent that designers have to judge whether a proof of a particular obligation is worth conducting. Even if hand proofs are… (More)

For many real time applications, it is impor tant to validate the information received from the sensors before entering higher levels of reasoning. This paper presents an any time probabilistic algorithm for validating the in formation provided by sensors. The sys tem consists of two Bayesian network mod els. The first one is a model of the dependen… (More)

- Sunil Vadera
- TKDD
- 2010

This article presents a new decision tree learning algorithm called CSNL that induces <b>C</b>ost-<b>S</b>ensitive <b>N</b>on-<b>L</b>inear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees… (More)

Decision tree induction has been has been widely studied and applied. In safety applications , such as determining whether a chemical process is safe, or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than the other class. Several authors have tackled this problem by developing… (More)

This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is… (More)