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.
Highlights
A supervised learning process is the machine learning task of training a function that maps an input to an output based on data points with known outputs
We proposed a novel data reduction algorithm that adapts to Label Distribution Learning (LDL) constraints
It simultaneously address the prototype selection and the label-specific feature selection with two objectives: finding an optimal subset of samples to improve the performance of the AA-kNN learner and selecting a subset of characteristics specific for each one of the output label. Both tasks have been addressed as search problems using an evolutionary algorithm, based on CHC, to optimize the solution
Summary
A supervised learning process is the machine learning task of training a function that maps an input to an output based on data points with known outputs. Classification is the process of predicting to which of a set of categories a new observation belongs. The purpose of classification is to achieve a model that will be able to classify the right class to an unknown pattern. There are an increasing number of problems where a pattern can have several labels simultaneously associated. Examples can be found in genetics [1], image classification [2], etc. The generalization of the classic classification is Multi-Label Learning (MLL) [3,4,5,6], where multiple labels can be assigned to each instance
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