Improving the ϵ-approximate algorithm for Probabilistic Classifier Chains

Probabilistic Classifier Chains are a multi-label classification method which has gained the attention of researchers in recent years. This is because of their ability to optimally estimate the entire joint conditional probability of a label combination through the product rule of probability. Their...

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Veröffentlicht in:	Knowledge and information systems Jg. 62; H. 7; S. 2709 - 2738
Hauptverfasser:	Fdez-Díaz, Miriam, Fdez-Díaz, Laura, Mena, Deiner, Montañés, Elena, Quevedo, José Ramón, Coz, Juan José del
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2020 Springer Nature B.V
Schlagworte:	Algorithms Chains Classifiers Computational efficiency Computer Science Computer simulation Computing costs Computing time Conditional probability Data Mining and Knowledge Discovery Database Management Heuristic methods Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business Optimization Regular Paper Searching Statistical analysis Multi-label Inference Classifier Chains approximate algorithm
ISSN:	0219-1377, 0219-3116
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Abstract	Probabilistic Classifier Chains are a multi-label classification method which has gained the attention of researchers in recent years. This is because of their ability to optimally estimate the entire joint conditional probability of a label combination through the product rule of probability. Their main drawback is that they require performing an exhaustive search in order to obtain Bayes optimal predictions. This means computing this probability for all possible label combinations before taking a label combination with the highest value of probability. This is the reason why several works have been published in recent years that avoid exploring all combinations, while maintaining optimality. Approaches such as greedy search, beam search and Monte Carlo reduce the computational cost, but at the cost of not ensuring Bayes optimal predictions (although, in general, they provide close to optimal solutions). Methods based on a heuristic search provide optimal predictions, but the computational time has not been as good as expected. In this respect, the ϵ -approximate algorithm has been found to be the best inference approach among those that provide Bayes optimal predictions, not only for its optimality, but also for its computational time. However, this paper both theoretically and experimentally shows that it sometimes performs some backtracking during the search for optimal predictions which may prolong the prediction time. The aim of this paper is thus to improve this algorithm by achieving a more direct search. Specifically, it enhances the criterion under which the next node to be expanded is chosen by adding heuristic information, although it is only applicable for linear-based models. The experiments carried out confirm that the improved ϵ -approximate algorithm explores fewer nodes and reduces the computational time of the original version.
AbstractList	Probabilistic Classifier Chains are a multi-label classification method which has gained the attention of researchers in recent years. This is because of their ability to optimally estimate the entire joint conditional probability of a label combination through the product rule of probability. Their main drawback is that they require performing an exhaustive search in order to obtain Bayes optimal predictions. This means computing this probability for all possible label combinations before taking a label combination with the highest value of probability. This is the reason why several works have been published in recent years that avoid exploring all combinations, while maintaining optimality. Approaches such as greedy search, beam search and Monte Carlo reduce the computational cost, but at the cost of not ensuring Bayes optimal predictions (although, in general, they provide close to optimal solutions). Methods based on a heuristic search provide optimal predictions, but the computational time has not been as good as expected. In this respect, the ϵ -approximate algorithm has been found to be the best inference approach among those that provide Bayes optimal predictions, not only for its optimality, but also for its computational time. However, this paper both theoretically and experimentally shows that it sometimes performs some backtracking during the search for optimal predictions which may prolong the prediction time. The aim of this paper is thus to improve this algorithm by achieving a more direct search. Specifically, it enhances the criterion under which the next node to be expanded is chosen by adding heuristic information, although it is only applicable for linear-based models. The experiments carried out confirm that the improved ϵ -approximate algorithm explores fewer nodes and reduces the computational time of the original version. Probabilistic Classifier Chains are a multi-label classification method which has gained the attention of researchers in recent years. This is because of their ability to optimally estimate the entire joint conditional probability of a label combination through the product rule of probability. Their main drawback is that they require performing an exhaustive search in order to obtain Bayes optimal predictions. This means computing this probability for all possible label combinations before taking a label combination with the highest value of probability. This is the reason why several works have been published in recent years that avoid exploring all combinations, while maintaining optimality. Approaches such as greedy search, beam search and Monte Carlo reduce the computational cost, but at the cost of not ensuring Bayes optimal predictions (although, in general, they provide close to optimal solutions). Methods based on a heuristic search provide optimal predictions, but the computational time has not been as good as expected. In this respect, the ϵ-approximate algorithm has been found to be the best inference approach among those that provide Bayes optimal predictions, not only for its optimality, but also for its computational time. However, this paper both theoretically and experimentally shows that it sometimes performs some backtracking during the search for optimal predictions which may prolong the prediction time. The aim of this paper is thus to improve this algorithm by achieving a more direct search. Specifically, it enhances the criterion under which the next node to be expanded is chosen by adding heuristic information, although it is only applicable for linear-based models. The experiments carried out confirm that the improved ϵ-approximate algorithm explores fewer nodes and reduces the computational time of the original version.
Author	Mena, Deiner Coz, Juan José del Quevedo, José Ramón Fdez-Díaz, Laura Montañés, Elena Fdez-Díaz, Miriam
Author_xml	– sequence: 1 givenname: Miriam surname: Fdez-Díaz fullname: Fdez-Díaz, Miriam organization: Artificial Intelligence Center, University of Oviedo at Gijón – sequence: 2 givenname: Laura surname: Fdez-Díaz fullname: Fdez-Díaz, Laura organization: Artificial Intelligence Center, University of Oviedo at Gijón – sequence: 3 givenname: Deiner surname: Mena fullname: Mena, Deiner organization: Dept. de Ingeniería en Telecomunicaciones e Informática, Universidad Tecnológica del Chocó – sequence: 4 givenname: Elena surname: Montañés fullname: Montañés, Elena email: montaneselena@uniovi.es organization: Artificial Intelligence Center, University of Oviedo at Gijón – sequence: 5 givenname: José Ramón surname: Quevedo fullname: Quevedo, José Ramón organization: Artificial Intelligence Center, University of Oviedo at Gijón – sequence: 6 givenname: Juan José del surname: Coz fullname: Coz, Juan José del organization: Artificial Intelligence Center, University of Oviedo at Gijón
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Cites_doi	10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 10.1145/2716262 10.1023/A:1007649029923 10.1016/j.patcog.2013.09.029 10.1016/j.patcog.2015.01.004 10.1007/s10994-012-5285-8 10.1002/widm.1185 10.1016/j.knosys.2017.03.015 10.1007/s10994-016-5600-x 10.1016/j.patcog.2006.12.019 10.1016/j.patcog.2013.10.006 10.1007/s10994-016-5593-5 10.1007/s10994-008-5064-8 10.1109/TKDE.2006.162 10.1007/s10994-009-5127-5 10.1007/s10994-011-5256-5 10.1007/s10994-013-5371-6 10.1007/3-540-44794-6_4 10.21236/ADA440081 10.1007/978-3-642-04174-7_17 10.1109/ICDM.2008.74 10.1007/978-3-642-23783-6_31 10.1109/ICTAI.2013.76 10.1109/FUZZ-IEEE.2015.7337815 10.1007/978-3-540-24775-3_5
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References	Cheng, Hüllermeier (CR2) 2009; 76 Lin, Weng, Keerthi (CR16) 2008; 9 Read, Martino, Olmos, Luengo (CR28) 2015; 48 Kumar, Vembu, Menon, Elkan (CR14) 2012; 2012 CR18 CR17 CR13 Zhang, Zhou (CR37) 2007; 40 Dembczyński, Cheng, Hüllermeier (CR4) 2010; 2010 CR12 CR34 CR33 CR10 Mena, Montañés, Quevedo, del Coz (CR20) 2017; 106 Read, Pfahringer, Holmes, Frank (CR31) 2011; 85 CR30 Schapire, Singer (CR32) 2000; 39 Kumar, Vembu, Menon, Elkan (CR15) 2013; 92 García, Herrera (CR9) 2008; 9 Mena, Quevedo, Montañés, del Coz (CR21) 2017; 126 CR3 Brier (CR1) 1950; 78 CR7 Fürnkranz, Hüllermeier, Loza Mencía, Brinker (CR8) 2008; 73 CR29 CR26 CR25 CR24 CR22 Gibaja, Ventura (CR11) 2015; 47 Montañés, Senge, Barranquero, Quevedo, del Coz, Hüllermeier (CR23) 2014; 47 Wu, Lin (CR35) 2017; 106 Dembczynski, Waegeman, Hüllermeier (CR6) 2012; 242 Read, Martino, Luengo (CR27) 2014; 47 Mena, Montañés, Quevedo, del Coz (CR19) 2016; 6 Zhang, Zhou (CR36) 2006; 18 Dembczyński, Waegeman, Cheng, Hüllermeier (CR5) 2012; 88 D Mena (1436_CR20) 2017; 106 J Read (1436_CR28) 2015; 48 ML Zhang (1436_CR36) 2006; 18 K Dembczyński (1436_CR4) 2010; 2010 D Mena (1436_CR19) 2016; 6 S García (1436_CR9) 2008; 9 E Montañés (1436_CR23) 2014; 47 1436_CR30 A Kumar (1436_CR14) 2012; 2012 W Cheng (1436_CR2) 2009; 76 1436_CR10 K Dembczyński (1436_CR5) 2012; 88 1436_CR17 1436_CR18 1436_CR33 1436_CR12 1436_CR34 1436_CR13 K Dembczynski (1436_CR6) 2012; 242 1436_CR3 A Kumar (1436_CR15) 2013; 92 1436_CR7 J Fürnkranz (1436_CR8) 2008; 73 E Gibaja (1436_CR11) 2015; 47 D Mena (1436_CR21) 2017; 126 J Read (1436_CR27) 2014; 47 RE Schapire (1436_CR32) 2000; 39 GW Brier (1436_CR1) 1950; 78 CJ Lin (1436_CR16) 2008; 9 1436_CR26 ML Zhang (1436_CR37) 2007; 40 1436_CR29 YP Wu (1436_CR35) 2017; 106 1436_CR22 1436_CR24 1436_CR25 J Read (1436_CR31) 2011; 85
References_xml	– ident: CR22 – volume: 242 start-page: 294 year: 2012 end-page: 299 ident: CR6 article-title: An analysis of chaining in multi-label classification publication-title: Front Artif Intell Appl – ident: CR18 – volume: 2010 start-page: 279 year: 2010 end-page: 286 ident: CR4 article-title: Bayes optimal multilabel classification via probabilistic classifier chains publication-title: ICML – ident: CR12 – ident: CR30 – volume: 78 start-page: 1 issue: 1 year: 1950 end-page: 3 ident: CR1 article-title: Verification of forecasts expressed in terms of probability publication-title: Mon Weather Rev doi: 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 – ident: CR10 – ident: CR33 – volume: 9 start-page: 627 issue: Apr year: 2008 end-page: 650 ident: CR16 article-title: Trust region Newton method for logistic regression publication-title: J Machine Learn Res – ident: CR29 – volume: 47 start-page: 52:1 issue: 3 year: 2015 end-page: 52:38 ident: CR11 article-title: A tutorial on multilabel learning publication-title: ACM Comput Surv doi: 10.1145/2716262 – volume: 39 start-page: 135 year: 2000 end-page: 168 ident: CR32 article-title: Boostexter: a boosting-based system for text categorization publication-title: Machine Learn doi: 10.1023/A:1007649029923 – ident: CR25 – volume: 47 start-page: 1494 issue: 3 year: 2014 end-page: 1508 ident: CR23 article-title: Dependent binary relevance models for multi-label classification publication-title: Pattern Recogn doi: 10.1016/j.patcog.2013.09.029 – volume: 2012 start-page: 665 year: 2012 end-page: 680 ident: CR14 article-title: Learning and inference in probabilistic classifier chains with beam search publication-title: ECML/PKDD – volume: 48 start-page: 2096 issue: 6 year: 2015 end-page: 2109 ident: CR28 article-title: Scalable multi-output label prediction: from classifier chains to classifier trellises publication-title: Pattern Recogn doi: 10.1016/j.patcog.2015.01.004 – volume: 88 start-page: 5 issue: 1–2 year: 2012 end-page: 45 ident: CR5 article-title: On label dependence and loss minimization in multi-label classification publication-title: Mach Learn doi: 10.1007/s10994-012-5285-8 – volume: 6 start-page: 215 issue: 6 year: 2016 end-page: 230 ident: CR19 article-title: An overview of inference methods in probabilistic classifier chains for multilabel classification publication-title: Wiley Interdiscip Rev Data Min Knowl Discov doi: 10.1002/widm.1185 – ident: CR3 – volume: 126 start-page: 78 year: 2017 end-page: 90 ident: CR21 article-title: A heuristic in A* for inference in nonlinear probabilistic classifier chains publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2017.03.015 – volume: 106 start-page: 671 issue: 5 year: 2017 end-page: 694 ident: CR35 article-title: Progressive random k-labelsets for cost-sensitive multi-label classification publication-title: Mach Learn doi: 10.1007/s10994-016-5600-x – volume: 40 start-page: 2038 issue: 7 year: 2007 end-page: 2048 ident: CR37 article-title: Ml-knn: a lazy learning approach to multi-label learning publication-title: Pattern Recogn doi: 10.1016/j.patcog.2006.12.019 – volume: 47 start-page: 1535 issue: 3 year: 2014 end-page: 1546 ident: CR27 article-title: Efficient monte carlo methods for multi-dimensional learning with classifier chains publication-title: Pattern Recogn doi: 10.1016/j.patcog.2013.10.006 – ident: CR17 – volume: 106 start-page: 143 issue: 1 year: 2017 end-page: 169 ident: CR20 article-title: A family of admissible heuristics for A* to perform inference in probabilistic classifier chains publication-title: Mach Learn doi: 10.1007/s10994-016-5593-5 – ident: CR13 – volume: 73 start-page: 133 year: 2008 end-page: 153 ident: CR8 article-title: Multilabel classification via calibrated label ranking publication-title: Mach Learn doi: 10.1007/s10994-008-5064-8 – ident: CR34 – volume: 18 start-page: 1338 year: 2006 end-page: 1351 ident: CR36 article-title: Multilabel neural networks with applications to functional genomics and text categorization publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2006.162 – volume: 76 start-page: 211 issue: 2–3 year: 2009 end-page: 225 ident: CR2 article-title: Combining instance-based learning and logistic regression for multi-label classification publication-title: Mach Learn doi: 10.1007/s10994-009-5127-5 – ident: CR7 – volume: 9 start-page: 2677 issue: 12 year: 2008 end-page: 2694 ident: CR9 article-title: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons publication-title: J Machine Learn Res – ident: CR26 – ident: CR24 – volume: 85 start-page: 333 issue: 3 year: 2011 end-page: 359 ident: CR31 article-title: Classifier chains for multi-label classification publication-title: Mach Learn doi: 10.1007/s10994-011-5256-5 – volume: 92 start-page: 65 issue: 1 year: 2013 end-page: 89 ident: CR15 article-title: Beam search algorithms for multi-label learning publication-title: Mach Learn doi: 10.1007/s10994-013-5371-6 – volume: 2010 start-page: 279 year: 2010 ident: 1436_CR4 publication-title: ICML – volume: 126 start-page: 78 year: 2017 ident: 1436_CR21 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2017.03.015 – volume: 9 start-page: 2677 issue: 12 year: 2008 ident: 1436_CR9 publication-title: J Machine Learn Res – ident: 1436_CR26 – ident: 1436_CR3 doi: 10.1007/3-540-44794-6_4 – volume: 39 start-page: 135 year: 2000 ident: 1436_CR32 publication-title: Machine Learn doi: 10.1023/A:1007649029923 – volume: 40 start-page: 2038 issue: 7 year: 2007 ident: 1436_CR37 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2006.12.019 – ident: 1436_CR10 doi: 10.21236/ADA440081 – ident: 1436_CR17 – ident: 1436_CR30 doi: 10.1007/978-3-642-04174-7_17 – volume: 106 start-page: 671 issue: 5 year: 2017 ident: 1436_CR35 publication-title: Mach Learn doi: 10.1007/s10994-016-5600-x – volume: 47 start-page: 52:1 issue: 3 year: 2015 ident: 1436_CR11 publication-title: ACM Comput Surv doi: 10.1145/2716262 – ident: 1436_CR29 doi: 10.1109/ICDM.2008.74 – ident: 1436_CR22 doi: 10.1007/978-3-642-23783-6_31 – volume: 18 start-page: 1338 year: 2006 ident: 1436_CR36 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2006.162 – ident: 1436_CR13 doi: 10.1109/ICTAI.2013.76 – volume: 106 start-page: 143 issue: 1 year: 2017 ident: 1436_CR20 publication-title: Mach Learn doi: 10.1007/s10994-016-5593-5 – ident: 1436_CR34 – ident: 1436_CR7 – volume: 48 start-page: 2096 issue: 6 year: 2015 ident: 1436_CR28 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2015.01.004 – ident: 1436_CR33 – volume: 73 start-page: 133 year: 2008 ident: 1436_CR8 publication-title: Mach Learn doi: 10.1007/s10994-008-5064-8 – ident: 1436_CR25 – volume: 242 start-page: 294 year: 2012 ident: 1436_CR6 publication-title: Front Artif Intell Appl – ident: 1436_CR24 doi: 10.1109/FUZZ-IEEE.2015.7337815 – volume: 76 start-page: 211 issue: 2–3 year: 2009 ident: 1436_CR2 publication-title: Mach Learn doi: 10.1007/s10994-009-5127-5 – ident: 1436_CR18 – volume: 6 start-page: 215 issue: 6 year: 2016 ident: 1436_CR19 publication-title: Wiley Interdiscip Rev Data Min Knowl Discov doi: 10.1002/widm.1185 – volume: 2012 start-page: 665 year: 2012 ident: 1436_CR14 publication-title: ECML/PKDD – volume: 47 start-page: 1535 issue: 3 year: 2014 ident: 1436_CR27 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2013.10.006 – volume: 47 start-page: 1494 issue: 3 year: 2014 ident: 1436_CR23 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2013.09.029 – volume: 9 start-page: 627 issue: Apr year: 2008 ident: 1436_CR16 publication-title: J Machine Learn Res – ident: 1436_CR12 doi: 10.1007/978-3-540-24775-3_5 – volume: 78 start-page: 1 issue: 1 year: 1950 ident: 1436_CR1 publication-title: Mon Weather Rev doi: 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 – volume: 85 start-page: 333 issue: 3 year: 2011 ident: 1436_CR31 publication-title: Mach Learn doi: 10.1007/s10994-011-5256-5 – volume: 88 start-page: 5 issue: 1–2 year: 2012 ident: 1436_CR5 publication-title: Mach Learn doi: 10.1007/s10994-012-5285-8 – volume: 92 start-page: 65 issue: 1 year: 2013 ident: 1436_CR15 publication-title: Mach Learn doi: 10.1007/s10994-013-5371-6
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Snippet	Probabilistic Classifier Chains are a multi-label classification method which has gained the attention of researchers in recent years. This is because of their...
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SubjectTerms	Algorithms Chains Classifiers Computational efficiency Computer Science Computer simulation Computing costs Computing time Conditional probability Data Mining and Knowledge Discovery Database Management Heuristic methods Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business Optimization Regular Paper Searching Statistical analysis
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Title	Improving the ϵ-approximate algorithm for Probabilistic Classifier Chains
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