Metadata-Version: 2.1
Name: DeepCoreML
Version: 0.4.3
Summary: A collection of Machine Learning techniques for data management, engineering and augmentation.
Home-page: https://github.com/lakritidis/DeepCoreML
Author: Leonidas Akritidis
Author-email: lakritidis@ihu.gr
Maintainer: Leonidas Akritidis
Maintainer-email: lakritidis@ihu.gr
License: Apache
Keywords: data engineering,data management,text vectorization,text processing,dimensionality reduction,imbalanced data,machine learning,deep learning
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: joblib
Requires-Dist: sdv
Requires-Dist: torch>=2.0.0
Requires-Dist: transformers>=4.29.0
Requires-Dist: scikit-learn>=1.4.0
Requires-Dist: xgboost
Requires-Dist: imblearn>=0.0
Requires-Dist: rdt<2.0,>=1.3.0
Requires-Dist: tqdm

<p>DeepCoreML is a collection of Machine Learning techniques for data management, engineering, and augmentation. More specifically, DeepCoreML includes modules for:</p><ul><li>Data management</li><li>Text data preprocessing</li><li>Text representation, vectorization, embeddings</li><li>Dimensionality reduction</li><li>Generative modeling</li><li>Imbalanced datasets</li></ul><p><b>Licence:</b> Apache License, 2.0 (Apache-2.0)</p><p><b>Dependencies:</b>NumPy, pandas, Natural Language Toolkit (nltk), Matplotlib, seaborn, Gensim, joblib, Reversible Data Transforms(RDT), bs4, scikit-learn, imblearn, pytorch, transformers, Synthetic Data Vault</p><p><b>GitHub repository:</b> <a href="https://github.com/lakritidis/DeepCoreML">https://github.com/lakritidis/DeepCoreML</a></p><p><b>Publications:</b><ul><li>L. Akritidis, P. Bozanis, "A Clustering-Based Resampling Technique with Cluster Structure Analysis forSoftware Defect Detection in Imbalanced Datasets", Information Sciences, vol. 674, pp. 120724, 2024.</li><li>L. Akritidis, A. Fevgas, M. Alamaniotis, P. Bozanis, "Conditional Data Synthesis with Deep Generative Models for Imbalanced Dataset Oversampling", In Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 444-451, 2023, 2023.</li></ul></p>
