# Name: Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification # Authors: Jose J. Valero-Mas [I1] , Antonio Javier Gallego [I1], Pablo Alonso-Jiménez [I2], Xavier Serra [I2] [I1] University Institute for Computer Research, University of Alicante, Spain [I2] Music Technology Group, Universitat Pompeu Fabra, Spain # Description: Supplementary material for the submission to Pattern Recognition for reproducible research and further development and exploitation of the proposed methods # Proposed Multilabel Prototype Generation methods: - MRHC : Direct implementation of the work in [1] - MChen : Novel adaptation of the Chen method [2] to the multilabel space introduced in this work - MRSP1 : Novel adaptation of the RSP (version 1) PG method [3] to the multilabel space introduced in this work - MRSP2 : Novel adaptation of the RSP (version 2) PG method [3] to the multilabel space introduced in this work - MRSP3 : Novel adaptation of the RSP (version 3) PG method [3] to the multilabel space introduced in this work # Multilabel Classifiers implemented (for the experimental evaluation): - Binary Relevance kNN (BRkNN) - Label Powerset kNN (LP-kNN) - Multilabel kNN (MLkNN) # Corpora considered: - 12 benchmarking corpora with three levels of label noise # Usage (reproduction of the results in the manuscript): 1) Install dependecies: $ pip install -r requirements.txt 2) Start experimentation: $ python experiments.py # References: [1] Ougiaroglou, S., Filippakis, P., & Evangelidis, G. (2021, September). Prototype Generation for Multi-label Nearest Neighbours Classification. In International Conference on Hybrid Artificial Intelligence Systems (pp. 172-183). Springer, Cham. [2] Chen, C. H., & Jóźwik, A. (1996). A sample set condensation algorithm for the class sensitive artificial neural network. Pattern Recognition Letters, 17(8), 819-823. [3] Sánchez, J. S. (2004). High training set size reduction by space partitioning and prototype abstraction. Pattern Recognition, 37(7), 1561-1564.