Fuzzy-Citation-KNN: a fuzzy nearest neighbor approach for multi-instance classification Villar Castro, Pedro Montes Soldado, Rosa Ana Sánchez López, Ana María Herrera Triguero, Francisco multi-instance classification fuzzy systems This contribution deals with multi-instance classification, where the labeled data samples are bags composed on instances instead of labeled instances as in standard classification. Every bag contains a number of traditional instances (described by a number of attributes) and the number of instances is not usually the same in all the bags. So, the whole bag is labeled but the instances that compose the bag are not individually labeled. We propose a fuzzy sets based extension of the well known algorithm called Citation-KNN, a reference method in multi-instance classification. Citation-KNN uses two types of examples in the classification rule: neighbors and citers of the bag to be classified. We analyze two versions of our proposal, one of them using both neighbors and citers, and the other one using only neighbors. Our approach uses the Hausdorff distance and it is based on the FuzzyKNN algorithm. Several data-sets from KEEL data-set repository are used in the experimental study and we compare our proposals with the original Citation-KNN algorithm. 2024-02-09T12:04:11Z 2024-02-09T12:04:11Z 2016-11-10 conference output P. Villar, R. Montes, A. M. Sánchez and F. Herrera, "Fuzzy-Citation-KNN: A fuzzy nearest neighbor approach for multi-instance classification," 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada, 2016, pp. 946-952, doi: 10.1109/FUZZ-IEEE.2016.7737790. https://hdl.handle.net/10481/88864 10.1109/FUZZ-IEEE.2016.7737790 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional