In contrast to ever increasing volumes of automatically generated data, human annotation capacities remain limited. Thus, fast active learning approaches that allow the efficient allocation of annotation efforts gain in importance. Furthermore, cost-sensitive applications such as fraud detection pose the additional challenge of differing misclassification costs between classes. Unfortunately, t...

One of the most challenging problems in computational advertising is the prediction of ad click and conversion rates for bidding in online advertising auctions. State-ofthe-art prediction methods include using the maximum entropy framework (also known as logistic regression) and log linear models. However, one unaddressed problem in the previous approaches is the existence of highly non-uniform...

Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes are abundant, making them an overrepresented majority, and data of other classes are scarce, making them an underrepresented minority. This imbalance makes it challenging for a classifier to appropriately learn the discriminating boundaries of the majority and minority c...

In this paper, we propose a new supervised learning method whereby information is controlled by the associated cost in an intermediate layer, and in an output layer, errors between targets and outputs are minimized. In the intermediate layer, competition is realized by maximizing mutual information between input patterns and competitive units with Gaussian functions. The process of information ...

Active Learning (AL) is a selective sampling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually annotated. For the annotation of natural language data, cost efficiency is usually measured in terms of the number of tokens to be considered. This measure, assuming uniform costs for all tokens involved, is, from a lingu...

Gene conversion, a non-reciprocal transfer of genetic information from one sequence to another, is a biological process whose importance in affecting both short-term and long-term evolution cannot be overemphasized. Knowing where gene conversion has occurred gives us important insights into gene duplication and evolution in general. In this paper we present an ensemble-based learning method for...

During the past decade, machine learning algorithms have become commonplace in large-scale real-world industrial applications. In these settings, the computation time to train and test machine learning algorithms is a key consideration. At training-time the algorithms must scale to very large data set sizes. At testing-time, the cost of feature extraction can dominate the CPU runtime. Recently,...

This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to formulate this as a Markov Decision Process in which the transition model is learned from the training data. Specifically, we assume a set of training examples in which all attributes (and the true class) have been mea...