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Abstract. The CoIL Challenge 2000 data mining competition attracted a wide variety of solutions, both in terms of approaches and performance. The goal of the competition was to predict who would be interested in buying a specific insurance product and to explain why people would buy. Unlike in most other competitions, the majority of participants provided a report describing the path to their solution. In this article we use the framework of bias-variance decomposition of error to analyze what caused the wide range of prediction performance. We characterize the challenge problem to make it comparable to other problems and evaluate why certain methods work or not. We also include an evaluation of the submitted explanations by a marketing expert. We find that variance is the key component of error for this problem. Participants use various strategies in data preparation and model development that reduce variance error, such as feature selection and the use of simple, robust and low variance learners like Naive Bayes. Adding constructed features, modeling with complex, weak bias learners and extensive fine tuning by the participants often increase the variance error.

Aims: Agronomy and breeding actively search for options to enhance cereal grain Zn density. Quantifying internal (re-)allocation of Zn as affected by soil and crop management or genotype is crucial. We present experiments supporting the development of a conceptual model of whole plant Zn allocation and re-allocation in rice.Methods: Two solution culture experiments using 70Zn applications at different times during crop development and an experiment on within-grain distribution of Zn are reported. In addition, results from two earlier published experiments are re-analyzed and re-interpreted.Results: A budget analysis showed that plant zinc accumulation during grain filling was larger than zinc allocation to the grains. Isotope data showed that zinc taken up during grain filling was only partly transported directly to the grains and partly allocated to the leaves. Zinc taken up during grain filling and allocated to the leaves replaced zinc re-allocated from leaves to grains. Within the grains, no major transport barrier was observed between vascular tissue and endosperm. At low tissue Zn concentrations, rice plants maintained concentrations of about 20 mg Zn kg−1 dry matter in leaf blades and reproductive tissues, but let Zn concentrations in stems, sheath, and roots drop below this level. When plant zinc concentrations increased, Zn levels in leaf blades and reproductive tissues only showed a moderate increase while Zn levels in stems, roots, and sheaths increased much more and in that order.Conclusions: In rice, the major barrier to enhanced zinc allocation towards grains is between stem and reproductive tissues. Enhancing root to shoot transfer will not contribute proportionally to grain zinc enhancement.

There is a range of techniques available to reverse engineer software designs from source code. However, these approaches generate highly detailed representations. The condensing of reverse engineered representations into more highlevel design information would enhance the understandability of reverse engineered diagrams. This paper describes an automated approach for condensing reverse engineered diagrams into diagrams that look as if they are constructed as forward designed UML models. To this end, we propose a machine learning approach. The training set of this approach consists of a set of forward designed UML class diagrams and reverse engineered class diagrams (for the same system). Based on this training set, the method 'learns' to select the key classes for inclusion in the class diagrams. In this paper, we study a set of nine classification algorithms from the machine learning community and evaluate which algorithms perform best for predicting the key classes in a class diagram.

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