Financial distress prediction is crucial in the financial domain because of its implications for banks, businesses, and corporations. Serious losses may occur poor prediction. As a result, significant efforts have been made to develop models that can assist decision-makers anticipate events before they avoid bankruptcy, thereby helping improve quality such tasks. Because usual highly imbalanced...

Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a costsensitive perceptron learning rule for non-sep...

This thesis theoretically discusses the abilities of three commonly used classifier learning methods and optimization techniques to copewith characteristics of real-world classification problems, more specifically varying misclassification costs, imbalanced data sets and varying degrees of hardness of class boundaries. From these discussions a universally applicable optimization framework is de...

This paper presents a cost-sensitive Question-Answering (QA) framework for learning a nine-layer And-Or graph (AoG) from web images, which explicitly represents object categories, poses, parts, and detailed structures within the parts in a compositional hierarchy. The QA framework is designed to minimize an overall risk, which trades off the loss and query costs. The loss is defined for nodes i...

Many learning systems must confront the problem of run time after learning being greater than run time before learning. This utility problem has been a particular focus of research in explanation-based learning. In past work we have examined an approach to the utility problem that is based on restricting the expressiveness of the rule language so as to guarantee polynomial bounds on the cost of...

In some real-world applications, it is time-consuming or expensive to collect much labeled data, while unlabeled data is easier to obtain. Many semi-supervised learning methods have been proposed to deal with this problem by utilizing the unlabeled data. On the other hand, on some datasets, misclassifying different classes causes different costs, which challenges the common assumption in classi...