Neighborhood Learning (NL) is a paradigm covering theories and techniques of
neighborhood, which facilitates data organization, representation and generalization. While …

This paper develops a framework to learn and predict design commands based upon
building information modeling (BIM) event log data stored in Autodesk Revit journal files …

Prototype Generation (PG) methods are typically considered for improving the efficiency of
the k-Nearest Neighbour (k NN) classifier when tackling high-size corpora. Such …

Self-training method is one of the relatively successful methodologies of semi-supervised
classification. It can exploit both labeled data and unlabeled data to train a satisfactory …

The detection of faulty machinery and its automated diagnosis is an industrial priority
because efficient fault diagnosis implies efficient management of the maintenance times …

Training support vector machines (SVMs) for pixel-based feature extraction purposes from
aerial images requires selecting representative pixels (instances) as a training dataset. In …

A very common practice to speed up instance based classifiers is to reduce the size of their
training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen …

Instance selection aims to search for the best patterns in the training set and main instance
selection methods include condensation methods, edition methods and hybrid methods …

The k-nearest neighbour rule is commonly considered for classification tasks given its
straightforward implementation and good performance in many applications. However, its …

The multi-label classification task has gained a lot of attention in the last decade thanks to its
good application to many real-world problems where each object could be attached to …