Hedonic pricing models attempt to model a relationship between object attributes and the object's price. Traditional hedonic pricing models are often parametric models that suffer from misspecification. In this paper we create these models by means of boosted CART models. The method is explained...

The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the brand...

In this report a support system for predicting end prices on eBay is proposed. The end price predictions are based on the item descriptions found in the item listings of eBay, and on some numerical item features. The system uses text mining and boosting algorithms from the field of machine...

The monotonicity constraint is a common side condition imposed on modeling problems as diverse as hedonic pricing, personnel selection and credit rating. Experience tells us that it is not trivial to generate artificial data for supervised learning problems when the monotonicity constraint...

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved...

Dilworth's theorem establishes a link between a minimal path cover and a maximal antichain in a digraph. A new proof for Dilworth's theorem is given. Moreover an algorithm to find both the path cover and the antichain, as considered in the theorem, is presented.