@misc{10481/62618, year = {2020}, month = {4}, url = {http://hdl.handle.net/10481/62618}, abstract = {Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. However, it suffers from several handicaps: it needs large storage requirements, it is not efficient predicting and presents a low tolerance to noise. The purpose of this paper is to mitigate these effects by adding a data reduction stage. The technique devised, called Prototype selection and Label-Specific Feature Evolutionary Optimization for LDL (ProLSFEO-LDL), is a novel method to simultaneously address the prototype selection and the label-specific feature selection pre-processing techniques. Both techniques pose a complex optimization problem with a huge search space. Therefore, we have proposed a search method based on evolutionary algorithms that allows us to obtain a solution to both problems in a reasonable time. The effectiveness of the proposed ProLSFEO-LDL method is verified on several real-world LDL datasets, showing significant improvements in comparison with using raw datasets.}, organization = {This work is supported by the Spanish National Research Project TIN2017-89517-P.}, publisher = {MDPI}, keywords = {Label distribution learning}, keywords = {Evolutionary optimization}, keywords = {Protoype selection}, keywords = {Label-specific feature}, keywords = {Machine learning}, title = {ProLSFEO-LDL: Prototype Selection and LabelSpecific Feature Evolutionary Optimization for Label Distribution Learning}, doi = {10.3390/app10093089}, author = {González, Manuel and García, Salvador}, }