Compressed kNN

Repository for the Compressed kNN (CS-kNN) algorithm implemented in MOA.

For more informations about MOA, check out the official website: http://moa.cms.waikato.ac.nz

Citing CS-kNN

To cite the CS-kNN in a publication, please cite the following paper:

Maroua Bahri, Albert Bifet, Silviu Maniu, Rodrigo Fernandes de Mello, Nikolaos Tziortziotis. Compressed k-Nearest Neighbors Ensembles for Evolving Data Streams. In the 24th European Conference on Artificial Intelligence (ECAI), 2020.

Important source files

The implementation and related codes used in this work are the following:

• CS-kNN.java: the compressed sensing k-nearest neighbors using the random projection internally.
• CS-filter.java: the compressed sensing used as a filter.

How to execute it

To test the CS-kNN, you can copy and paste the following command in the interface (right click the configuration text edit and select "Enter configuration”). Sample command:

EvaluatePrequential -l (lazy.CS_kNN -d 5 -f 500) -s (ArffFileStream -f /pathto/tweet500.arff) -e BasicClassificationPerformanceEvaluator

Explanation: this command executes CS-kNN prequential evaluation precising the output and input dimensionality, d and f respectively on the tweet500 dataset (-f tweet1.arff). Make sure to extract the tweet500.arff dataset, and setting -f to its location (pathto), before executing the command.

Datasets used in the original paper

The datasets used in this work are compressed and available at the root directory.