2018
Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César
Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning Journal Article
In: Expert Systems with Applications, vol. 109, pp. 114-130, 2018, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: Binary relevance, Label powerset, Multi-label classification, Prototype selection, RAkEL
@article{Arnaiz-González2018,
title = {Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning},
author = {Álvar Arnaiz-González and José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio},
url = {https://www.sciencedirect.com/science/article/pii/S0957417418303087},
doi = {10.1016/j.eswa.2018.05.017},
issn = {0957-4174},
year = {2018},
date = {2018-11-01},
journal = {Expert Systems with Applications},
volume = {109},
pages = {114-130},
abstract = {In this paper, the focus is on the application of prototype selection to multi-label data sets as a preliminary stage in the learning process. There are two general strategies when designing Machine Learning algorithms that are capable of dealing with multi-label problems: data transformation and method adaptation. These strategies have been successfully applied in obtaining classifiers and regressors for multi-label learning. Here we investigate the feasibility of data transformation in obtaining prototype selection algorithms for multi-label data sets from three prototype selection algorithms for single-label. The data transformation methods used were: binary relevance, dependent binary relevance, label powerset, and random k-labelsets. The general conclusion is that the methods of prototype selection obtained using data transformation are not better than those obtained through method adaptation. Moreover, prototype selection algorithms designed for multi-label do not do an entirely satisfactory job, because, although they reduce the size of the data set, without affecting significantly the accuracy, the classifier trained with the reduced data set does not improve the accuracy of the classifier when it is trained with the whole data set.},
keywords = {Binary relevance, Label powerset, Multi-label classification, Prototype selection, RAkEL},
pubstate = {published},
tppubtype = {article}
}
In this paper, the focus is on the application of prototype selection to multi-label data sets as a preliminary stage in the learning process. There are two general strategies when designing Machine Learning algorithms that are capable of dealing with multi-label problems: data transformation and method adaptation. These strategies have been successfully applied in obtaining classifiers and regressors for multi-label learning. Here we investigate the feasibility of data transformation in obtaining prototype selection algorithms for multi-label data sets from three prototype selection algorithms for single-label. The data transformation methods used were: binary relevance, dependent binary relevance, label powerset, and random k-labelsets. The general conclusion is that the methods of prototype selection obtained using data transformation are not better than those obtained through method adaptation. Moreover, prototype selection algorithms designed for multi-label do not do an entirely satisfactory job, because, although they reduce the size of the data set, without affecting significantly the accuracy, the classifier trained with the reduced data set does not improve the accuracy of the classifier when it is trained with the whole data set.