These are Hierarchical Classification datasets of Transposable Elements suitable for Machine Learning algorithms. These datasets were used in several papers published in top tier conferences, such as The International Joint Conference on Neural Networks (IJCNN), The IEEE Congress on Evolutionary Computation (CEC) and The Genetic and Evolutionary Computation Conference (GECCO).
A list of papers that used this data:
1 - A Genetic Algorithm for Transposable Elements Hierarchical Classification Rule Induction (https://ieeexplore.ieee.org/abstract/document/8477642)
2 - A Lexicographic Genetic Algorithm for Hierarchical Classification Rule Induction (https://dl.acm.org/doi/abs/10.1145/3321707.3321863)
3 - Hierarchical and Non-Hierarchical Classification of Transposable Elements with a Genetic Algorithm (https://periodicos.ufmg.br/index.php/jidm/article/view/401)
4 - Strategies for Selection of Positive and Negative Instances in the Hierarchical Classification of Transposable Elements (https://ieeexplore.ieee.org/abstract/document/8575650)
5 - Hierarchical Classification of Transposable Elements with a Weighted Genetic Algorithm (https://link.springer.com/chapter/10.1007/978-3-030-30241-2_61)
6 - Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks (https://ieeexplore.ieee.org/abstract/document/8489461)
7 - Stacking Methods for Hierarchical Classification (https://ieeexplore.ieee.org/abstract/document/8260647)
8 - Top-down strategies for hierarchical classification of transposable elements with neural networks (https://ieeexplore.ieee.org/abstract/document/7966165)