In medical diagnosis doctors must often determine what medical tests (e.g., X-ray, blood tests) should be ordered for a patient to minimize the total cost of medical tests and misdiagnosis. In this paper, we design cost-sensitive machine learning algorithms to model this learning and diagnosis process. Medical tests are like attributes in machine learning whose values may be obtained at cost (a...

Many algorithms in decision tree learning are not designed to handle numeric valued attributes very well. Therefore, discretization of the continuous feature space has to be carried out. In this article we introduce the concept of cost sensitive discretization as a preprocessing step to induction of a classifier and as an elaboration of the error-based discretization method to obtain an optimal...

This paper explores two techniques for boosting cost-sensitive trees. The two techniques diier in whether the misclassiication cost information is utilized during training. We demonstrate that each of these techniques is good at diierent aspects of cost-sensitive classiications. We also show that both techniques provide a means to overcome the weaknesses of their base cost-sensitive tree induct...

We present an efficient and effective method which extends the Boosting family of classifiers to allow the weighted classes. Typically classifiers do not treat individual classes separately. For most real world applications, this is not the case, not all classes have the same importance. The accuracy of a particular class can be more critical than others. In this paper we extend the mathematica...

We introduce cost-sensitive regression as a way to introduce information obtained by planning as background knowledge into a relational reinforcement learning algorithm. By offering a trade-off between using knowledge rich, but computationally expensive knowledge resulting from planning like approaches such as minimax search and computationally cheap, but possibly incorrect generalizations, the...

For many classification tasks a large number of instances available for training are unlabeled and the cost associated with the labeling process varies over the input space. Meanwhile, virtually all these problems require classifiers that minimize a nonuniform loss function associated with the classification decisions (rather than the accuracy or number of errors). For example, to train pattern...

SCA(Side-channel analysis) is a well-known method to recover the sensitive data stored in security products. Meanwhile numerous countermeasures for hardware implementation of cryptographic algorithms are proposed to protect the internal data against this attack fortunately. However, some designs are not aware that the protection of the plaintext and ciphertext is also crucial. In this work, we ...

Predictive models are often used for real-time decision making. However, typical machine learning techniques ignore feature evaluation cost, and focus solely on the accuracy of the machine learning models obtained utilizing all the features available. We develop algorithms and indexes to support cost-sensitive prediction, i.e., making decisions using machine learning models taking feature evalu...

Predictive models are often used for real-time decision making. However, typical machine learning techniques ignore feature evaluation cost, and focus solely on the accuracy of the machine learning models obtained utilizing all the features available. We develop algorithms and indexes to support cost-sensitive prediction, i.e., making decisions using machine learning models taking feature evalu...

In many classification settings, mistakes incur different application-dependent penalties based on the predicted and actual class labels. Costsensitive classifiers minimizing these penalties are needed. We propose a robust minimax approach for producing classifiers that directly minimize the cost of mistakes as a convex optimization problem. This is in contrast to previous methods that minimize...