Tabular data is hard to acquire and is subject to missing values. This paper introduces a
novel approach for generating and imputing mixed-type (continuous and categorical) tabular …

The curse of dimensionality causes the well-known and widely discussed problems for
machine learning methods. There is a hypothesis that using the Manhattan distance and …

The performance of a support vector machine (SVM) depends highly on the selection of the
kernel function type and relevant parameters. To choose the kernel parameters properly …

Dimensionality reduction (DR) aims at faithfully and meaningfully representing high-
dimensional (HD) data into a low-dimensional (LD) space. Recently developed neighbor …

Decision trees are popular machine learning models that are simple to build and easy to
interpret. Even though algorithms to learn decision trees date back to almost 50 years, key …

Novel machine learning methods for tabular data generation are often developed on small
datasets which do not match the scale required for scientific applications. We investigate a …

In this paper, we introduce Lorentzian distance metric into classification problem. Here we
benefit from the interesting properties of the distance metric of the Lorentzian space. A …

We revisit binary decision trees from the perspective of partitions of the data. We introduce
the notion of partitioning function, and we relate it to the growth function and to the VC …

Stochastic Neighbor Embedding (SNE) and variants are dimensionality reduction (DR)
methods able to foil the curse of dimensionality to deliver outstanding experimental results …

K-nearest neighbor is one of the simplest and most intuitive binary classification methods
providing robust results on a wide range of data. However, classification results can be …