2015
Read, Jesse; Martino, Luca; Olmos, Pablo M; Luengo, David
Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises Artículo de revista
En: Pattern Recognition, vol. 48, no. 6, pp. 2096–2106, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian networks, classifer chains, Journal, Multi-label classification, multi-output prediction, structured inference
@article{Read2015b,
title = {Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises},
author = {Jesse Read and Luca Martino and Pablo M Olmos and David Luengo},
url = {http://www.sciencedirect.com/science/article/pii/S0031320315000084},
year = {2015},
date = {2015-06-01},
journal = {Pattern Recognition},
volume = {48},
number = {6},
pages = {2096--2106},
abstract = {Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modeling a fully cascaded chain. In particular, the methods' strategies for discovering and modeling a good chain structure constitutes a mayor computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.},
keywords = {Bayesian networks, classifer chains, Journal, Multi-label classification, multi-output prediction, structured inference},
pubstate = {published},
tppubtype = {article}
}
Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modeling a fully cascaded chain. In particular, the methods' strategies for discovering and modeling a good chain structure constitutes a mayor computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.
2014
Impedovo, Sebastiano; Liu, Cheng-Lin; Impedovo, Donato; Pirlo, Giuseppe; Read, Jesse; Martino, Luca; Luengo, David
Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains Artículo de revista
En: Pattern Recognition, vol. 47, no. 3, pp. 1535–1546, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification
@article{Impedovo2014b,
title = {Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains},
author = {Sebastiano Impedovo and Cheng-Lin Liu and Donato Impedovo and Giuseppe Pirlo and Jesse Read and Luca Martino and David Luengo},
url = {http://www.sciencedirect.com/science/article/pii/S0031320313004160},
year = {2014},
date = {2014-01-01},
journal = {Pattern Recognition},
volume = {47},
number = {3},
pages = {1535--1546},
abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance \textendash at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},
keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},
pubstate = {published},
tppubtype = {article}
}
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.
2013
Read, Jesse; Martino, Luca; Luengo, David
Eficient Monte Carlo Optimization for Multi-Label Classifier Chains Artículo en actas
En: ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing, Vancouver, 2013.
Resumen | BibTeX | Etiquetas: Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification
@inproceedings{Read2013,
title = {Eficient Monte Carlo Optimization for Multi-Label Classifier Chains},
author = {Jesse Read and Luca Martino and David Luengo},
year = {2013},
date = {2013-01-01},
booktitle = {ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing},
address = {Vancouver},
abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for nding a good chain sequence and performing ecient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},
keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for nding a good chain sequence and performing ecient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.