As one of the most popular machine learning methods, random forests have been
successfully applied to different data analysis tasks such as classification, regression and …

The purpose of the theoretical considerations and research conducted was to indicate the
instruments with which the quality of a dataset can be verified for the segmentation of …

Regular inspection and maintenance work is required to ensure the structural integrity of
historic structures, especially the masonry structures which are deteriorating due to ageing …

Wine is not just about taste; it represents your class & overall personality. But tasting the
same wine for a long time gets boring; sometimes, everyone needs something new, and for …

In this paper we present a novel clustering approach based on Random Forests, a popular
classification and regression technique whose usability in the clustering scenario has been …

In this paper we present a novel Random Forest Clustering approach, called Dissimilarity
Random Forest Clustering (DisRFC), which requires in input only pairwise dissimilarities …

Learning from label proportions (LLP), in which the training data is in the form of bags and
only the proportion of each class in each bag is available, has attracted wide interest in …

Many visual surveillance tasks, eg video summarisation, is conventionally accomplished
through analysing imagery-based features. Relying solely on visual cues for public …

In this paper we propose, a novel Random Forest-based similarity measure for clustering.
We build upon Tversky's ratio model definition of similarity [1] and specialize it to the …

Electricity trading is an effective measure to minimize carbon emissions and alleviate the
imbalance between reverse distribution of regional energy resources and power load …