Data mining techniques often ask for the resolution of optimization problems. Supervised
classification, and, in particular, support vector machines, can be seen as a paradigmatic …

In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

Data normalization is one of the pre-processing approaches where the data is either scaled
or transformed to make an equal contribution of each feature. The success of machine …

The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …

The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve
classification and pattern recognition tasks. Despite its high classification accuracy, this rule …

k-nearest neighbor (k NN) is a widely used learning algorithm for supervised learning tasks.
In practice, the main challenge when using k NN is its high sensitivity to its hyperparameter …

In this paper, a novel k-nearest neighbors (k NN) weighting strategy is proposed for handling
the problem of class imbalance. When dealing with highly imbalanced data, a salient …

Kernel machines and rough sets are two classes of commonly exploited learning
techniques. Kernel machines enhance traditional learning algorithms by bringing …

In multi-label classification, each instance is associated with a set of pre-specified labels.
One common approach is to use Binary Relevance (BR) paradigm to learn each label by a …

Prototype selection problem consists of reducing the size of databases by removing samples
that are considered noisy or not influential on nearest neighbour classification tasks …