Simulated allocation models (SAMs) are used to evaluate organ allocation policies. An important component of SAMs is a module that decides whether each potential recipient will accept an offered organ. The objective of this study was to develop and test accept-or-decline classifiers based on...

Record linkage, a part of data cleaning, is recognized as one of most expensive steps in data warehousing. Most record linkage (RL) systems employ a strategy of using blocking filters to reduce the number of pairs to be matched. A blocking filter consists of a number of blocking criteria. Until...

In this paper, we investigate the forecasting ability of the yield curve in terms of the U.S. real GDP cycle. More specifically, within a Machine Learning framework, we use data from a variety of short (treasury bills) and long term interest rates (bonds) for the period from 1976:Q3 to 2011:Q4...

The contention of this paper is that many social science research problems are too “wicked” to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is...

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been...

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied...

We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying...

In this paper a machine learning approach for classifying Arabic text documents is presented. To handle the high dimensionality of text documents, embeddings are used to map each document (instance) into R (the set of real numbers) representing the tri-gram frequency statistics profiles for a...