Decision tree algorithms are very popular in the area of data mining since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model. Over the past years, fuzzy decision tree algorithms have been proposed in order to handle the uncertainty in the data. Fuzzy decision tree algorithms have shown to outperform classical decision tree algorithms. This chapter investigates a fuzzy decision tree algorithm applied to the classification of gene expression data. The fuzzy decision tree algorithm is compared to a classical decision tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy decision tree algorithm outperforms the classical decision tree algorithm. However, compared to other commonly used classification algorithms, both decision tree algorithms are competitive, but they do not reach the accuracy values of the best performing classifier. One of the advantages of decision tree models including the fuzzy decision tree algorithm is however the simplicity and comprehensibility of the model as demonstrated in the chapter.

Thin film composition spreads of Mg-Ni were deposited by co-sputtering on micromachined Si-cantilevers. The investigated compositions range from about Mg ₆₀ Ni ₄₀ to about Mg ₈₀ Ni ₂₀ . Structural properties as well as mechanical stress before and after hydrogenation were measured with X-ray diffraction (XRD) and laser profilometry, respectively. The composition spreads were hydrogenated in a special pressure vessel, which allows measuring optically the hydrogen-induced deflection (stress-change) of 16 cantilevers as a function of hydrogen pressure and/or temperature. It was found that the hydrogen-induced stress is correlated with the composition and microstructure of the films. Highest hydrogen-induced stress changes were found for compositions close to the crystalline Mg ₂ Ni phase.