Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection

Context-Sensitive Decision Forests for Object Detection

In this paper we introduce Context-Sensitive Decision Forests A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem. They are tree-structured classifiers with the ability to access intermediate prediction (here: classification and regression) information during training and inference time. This intermediate prediction is av...

O2TD: (Near)-Optimal Off-Policy TD Learning

Temporal difference learning and Residual Gradient methods are the most widely used temporal difference based learning algorithms; however, it has been shown that none of their objective functions are optimal w.r.t approximating the true value function V . Two novel algorithms are proposed to approximate the true value function V . This paper makes the following contributions: • A batch algorit...

A cost-sensitive decision tree approach for fraud detection

With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credi...

An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning

Cost-Sensitive Decision Tree Learning for Forensic Classification

In some learning settings, the cost of acquiring features for classification must be paid up front, before the classifier is evaluated. In this paper, we introduce the forensic classification problem and present a new algorithm for building decision trees that maximizes classification accuracy while minimizing total feature costs. By expressing the ID3 decision tree algorithm in an information ...