ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning
Open Access
- 29 April 2020
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 10 (9), 3089
- https://doi.org/10.3390/app10093089
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.Keywords
This publication has 49 references indexed in Scilit:
- Lift: Multi-Label Learning with Label-Specific FeaturesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2014
- Facial Age Estimation by Learning from Label DistributionsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithmsSwarm and Evolutionary Computation, 2011
- DIAGNOSE EFFECTIVE EVOLUTIONARY PROTOTYPE SELECTION USING AN OVERLAPPING MEASUREInternational Journal of Pattern Recognition and Artificial Intelligence, 2009
- Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and TaxonomyEvolutionary Computation, 2009
- Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classesPattern Recognition Letters, 2008
- A memetic algorithm for evolutionary prototype selection: A scaling up approachPattern Recognition, 2008
- Hierarchical multi-label prediction of gene functionBioinformatics, 2006
- Learning multi-label scene classificationPattern Recognition, 2004
- Instance-based learning algorithmsMachine Learning, 1991