Georgios Douzas

Posts

My articles on open-source, machine learning, mathematics, and more.

Publications

Published work.

Coset space dimensional reduction and Wilson flux breaking of ten-dimensional N=1, E 8 gauge theory

We consider a N=1 supersymmetric E 8 gauge theory, defined in ten dimensions and we determine all four-dimensional gauge theories resulting from the generalized dimensional…

Coset space dimensional reduction and classification of semi-realistic particle physics models

Starting from a Yang-Mills-Dirac theory defined in ten dimensions we classify the semi-realistic particle physics models resulting from their Forgacs-Manton dimensional…

Effective data generation for imbalanced learning using conditional generative adversarial networks

Learning from imbalanced datasets is a frequent but challenging task for standard classification algorithms. Although there are different strategies to address this problem…

G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE

Traditional supervised machine learning classifiers are challenged to learn highly skewed data distributions as they are designed to expect classes to equally contribute to…

Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE

Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating…

Geometric SMOTE for regression

Learning from imbalanced data sets is known to be a challenging task. There are many proposals to tackle the challenge for classification problems, but regarding regression…

Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE

Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating…

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource…

Improving Imbalanced Land Cover Classification with K-Means SMOTE: Detecting and Oversampling Distinctive Minority Spectral Signatures

Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production…

Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE

Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle…

Improving the quality of predictive models in small data GSDOT: A new algorithm for generating synthetic data

In the age of the data deluge there are still many domains and applications restricted to the use of small datasets. The ability to harness these small datasets to solve…

Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification

In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite…

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle…

Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning

Learning from imbalanced datasets is challenging for standard algorithms, as they are designed to work with balanced class distributions. Although there are different…