@@ -74,6 +74,9 @@ in the folder "other_approaches" as Jupyter notebooks.

### SKLEARN Clustering

In the notebook "dbscan_clustering.ipynb" we explored the possibility to use clustering algorithms defined in sklearn in order to classify the given entities. Here we choose DBSCAN, as SKLEARN states it working well with inbalanced datasets. Unfortunetly, the approach did not yield good results and was therefore no longer pursuited.

### PyTorch Geometric Graph Neural Network

A second approach was the implementation of a graph neural network from the library pytorch_geometric, i.e. a deep learning approach. The idea was to use a graph neural network for classification based on the labels of the learning problems and the edges of the knowledge graph. The first step was to fit the network using the train data and the CrossEntropyLoss as metric and, after that, classify all individuals (even the ones used for training). The network computes a probability distribution over the labels for each individual and the individuals are assigned to the class with the highest probability. However, since the data are very imbalanced, all individuals are assigned to the negative (excluded) class and the F1-score was not very meaningful. Unfortunately, it was not possible to find a solution for this problem, hence this approach was no longer from our interest.

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### Prerequisites

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@@ -103,11 +106,11 @@ In the notebook "dbscan_clustering.ipynb" we explored the possibility to use clu