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A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

Published on DOI: https://doi.org/10.5753/eniac.2020.12128

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Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called low-level classification. On the other hand, the human (animal) brain performs both low and high orders of learning, and it has a facility in identifying pat-terns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as high-level classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results. In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure. We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.

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This project was builded with the next technologies.

• Python
• Jupyter
• Pyswarms
• Networkx
• Scikit-learn

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You need the next componenets to run this project.

• Docker. To install it follow these steps Click. On Ubuntu, you can run:

sudo apt-get install docker-ce docker-ce-cli containerd.io

• Visual Studio Code. To install it follow these steps Click. On Ubuntu, you can run:

sudo snap install code --classic

• Install the visual studio code extension "Remote - Containers"

Follow the next steps:

1. Run the visual studio code.
2. Open the folder where you clone the repository.
3. Click on the green button with this symbol in the bottom left of visual studio code "><".
4. Click on reopen in a container.
5. Execute "main.py".

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You can use the HLNB_BC as a classifier of scikit-learn. Just need train and predict.

classifier = HLNB_BC()
classifier.fit(dataset["data"], dataset["target"])
classifier.predict(dataset_test["data"])

Distributed under the GNU v3 License. See LICENSE for more information.

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Esteban Vilca - @ds_estebanvz - [email protected]

Project Link: https://github.com/estebanvz/hl_classification_bc

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