Discretization is the process of converting continuous values into discrete values. It is a preprocessing step of several machine learning and data mining algorithms and the quality of discretization may drastically affect the performance of these algorithms. In this study we propose a discretization algorithm, namely line fitting-based discretization (lFIT), based on the Ramer-Douglas-Peucker algorithm. It is a static, univariate, unsupervised, splittingbased, global, and incremental discretization method where intervals are determined based on the Ramer-Douglas-Peucker algorithm and the quality of partitioning is assessed based on the standard error of the estimate. To evaluate the performance of the proposed method, a set of experiments are conducted on ten benchmark datasets and the achieved results are compared to those obtained by eight state-of-the-art discretization methods. Experimental results show that lFIT achieves higher predictive accuracy and produces less number of inconsistency while it generates larger number of intervals. The obtained results are also validated through Friedman's test and Holm's post hoc test which revealed the fact that lFIT produces discretization schemes that statistically comply both with supervised and unsupervised discretization methods.

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Landslide susceptibility assessment is the problem of determining the likelihood of a landslide to occur in a particular area based on the geological and morphological properties of the area. In this study, we propose a method wherein skyline operator is used to model landslides and majority voting is used to assess landslide susceptibility. Experiments conducted on a real life data set showed that the proposed method achieves 83.07% classification accuracy and is superior over most commonly used techniques for landslide susceptibility assessment such as logistic regression, support vector machines and artificial neural network.

Landslide susceptibility assessment is the problem of determining the likelihood of a landslide occurrence in a particular area with respect to the geographical and morphological properties of the area. This paper presents a hybrid method, namely SkySlide, that incorporates clustering, skyline operator, classification and majority voting principle for region-scale landslide susceptibility assessment. Clustering and skyline operator are utilized to model landslides while classification and majority voting principle are utilized to assess landslide susceptibility. The contribution of the study is 2-fold. First, the proposed method requires properties of landslide-occurring data only to model landslides. Second, the proposed method is evaluated on imbalanced data and experimental results include performance metrics of imbalanced data. Experiments conducted on two real-life datasets show that clustering greatly improves performance of SkySlide. Experiments further demonstrate that SkySlide achieves higher class balance accuracy, Matthews correlation coefficient, geometric mean and bookmaker informedness scores compared with the most commonly used methods for landslide susceptibility assessment such as support vector machines, logistic regression and decision trees.

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