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The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems [chapter]

Supanika Leurcharusmee, Jirakom Sirisrisakulchai, Songsak Sriboonchitta, Thierry Denœux
2014 Studies in Computational Intelligence  
We applied the Classifier Chain (CC) method to transform the Generalized Maximum Entropy (GME) choice model from a single-label model to a multi-label model.  ...  We used Monte-Carlo simulations and occupational hazard data to compare the CC-GME model with other selected methodologies for multi-label problems using the Hamming Loss, Accuracy, Precision and Recall  ...  [4, 15] introduced the Maximum Entropy (ME) model for discrete choice problems.  ... 
doi:10.1007/978-3-319-13449-9_13 fatcat:3xhqdtfjpfayhgud2xwi26emii

Multi-label Methods for Prediction with Sequential Data [article]

Jesse Read, Luca Martino, Jaakko Hollmén
2016 arXiv   pre-print
In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data.  ...  If labels indices are considered as time indices, the problems can often be seen as equivalent.  ...  Acknowledgements This work was supported in part by the Aalto University AEF research programme http://energyefficiency.aalto.fi/en/  ... 
arXiv:1609.08349v2 fatcat:lk7zuvtq2rc3hhl6p43gmx2jba

Statistical methods in language processing

Steven Abney
2010 Wiley Interdisciplinary Reviews: Cognitive Science  
It is characterized by the use of stochastic models, substantial data sets, machine learning, and rigorous experimental evaluation.  ...  There has, however, been little penetration of the methods into general linguistics. The methods themselves are largely borrowed from machine learning and information theory.  ...  For that reason, generative methods that do not make independence assumptions, but explicitly model dependencies, are attractive. Such methods include random fields and maximum entropy models.  ... 
doi:10.1002/wcs.111 pmid:26302079 fatcat:qnockuwjdzagxjgjqev36kwsee

Error Detection in Broadcast News ASR Using Markov Chains [chapter]

Thomas Pellegrini, Isabel Trancoso
2011 Lecture Notes in Computer Science  
Based on the observation that many errors appear in bursts, we investigated the use of Markov Chains (MC) for their temporal modelling capabilities.  ...  MC classification performance was compared with a discriminative maximum entropy model (Maxent), currently used in our in-house decoder to estimate confidence measures, and also with Gaussian Mixture Models  ...  Acknowledgements This work was partially supported by FCT (INESC-ID multi-annual funding) through the PIDDAC Program funds, by the FCT REAP.PT project (CMU-PT/HuMach/-0053/2008) and the Vidivideo European  ... 
doi:10.1007/978-3-642-20095-3_6 fatcat:atofxyaq6zhavicfgpekgkvdbe

A Rate Distortion Approach for Semi-Supervised Conditional Random Fields

Yang Wang, Gholamreza Haffari, Shaojun Wang, Greg Mori
2009 Neural Information Processing Systems  
multi-class classification and sequence labeling problems.  ...  Our experimental results show the rate distortion approach outperforms standard l 2 regularization, minimum conditional entropy regularization as well as maximum conditional entropy regularization on both  ...  for a discriminative model, Oliver and Garg [24] have proposed maximum mutual information hidden Markov models (MMIHMM) of semi-supervised training for chain structured graph.  ... 
dblp:conf/nips/0003HWM09 fatcat:yppphxcwkvb3dgxyxaq2azbbey

Dynamic Ensemble Selection with Probabilistic Classifier Chains [chapter]

Anil Narassiguin, Haytham Elghazel, Alex Aussem
2017 Lecture Notes in Computer Science  
In this paper, we show that the label dependencies have to be captured explicitly and propose a DES method based on Probabilistic Classifier Chains.  ...  Recent studies have reformulated the DES problem as a multi-label classification problem and promising performance gains have been reported.  ...  DES as a multi-label classification problem The DES problem has recently been reformulated as a multi-label classification (MLC) problem [16, 17, 20] .  ... 
doi:10.1007/978-3-319-71249-9_11 fatcat:7uy52ol5ezbsbo33ih77qm32rq

A Review on Multi-Label Learning Algorithms

Min-Ling Zhang, Zhi-Hua Zhou
2014 IEEE Transactions on Knowledge and Data Engineering  
As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.  ...  Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously.  ...  2 ) + q 2 (q + m)) for training and O(dq) for testing. 4) Collective Multi-Label Classifier (CML): The basic idea of this algorithm is to adapt maximum entropy principle to deal with multi-label data  ... 
doi:10.1109/tkde.2013.39 fatcat:oqvq3cei4vatdld4j4bqeyc7ry

Experimental Comparison of Methods for Multi-label Classification in different Application Domains

Passent ElKafrawy, Amr Mausad, Heba Esmail
2015 International Journal of Computer Applications  
C4.5 tree classifier as a single-label base learner.  ...  Real-world applications have begun to adopt the multi-label paradigm.  ...  The modified entropy sums the entropies for each individual class label.  ... 
doi:10.5120/20083-1666 fatcat:crkr2jbpgfhtfhuj3mbeplush4

Multi-label ECG Signal Classification Based on Ensemble Classifier

Zhanquan Sun, Chaoli Wang, Yangyang Zhao, Chao Yan
2020 IEEE Access  
The model combines several multi-label classification methods to generate a high performance classifier. Mutual information is used to measure the weight of each classifier.  ...  To resolve the multi-label ECG signal classification problems, we propose a novel ensemble multi-label classification model in this paper.  ...  ACKNOWLEDGMENT The authors declare that they have no conflict of interest.  ... 
doi:10.1109/access.2020.3004908 fatcat:bjl5ugj7gbc3lhvs7tr5umlrn4

Identifying agreement and disagreement in conversational speech

Michel Galley, Kathleen McKeown, Julia Hirschberg, Elizabeth Shriberg
2004 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04  
We describe a statistical approach for modeling agreements and disagreements in conversational interaction.  ...  Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse.  ...  Acknowledgments We are grateful to Mari Ostendorf and Dustin Hillard for providing us with their agreement and disagreement labeled data.  ... 
doi:10.3115/1218955.1219040 dblp:conf/acl/GalleyMHS04 fatcat:mp2fk2id3fbv7p76eywheztp7u

Multi-label methods for prediction with sequential data

Jesse Read, Luca Martino, Jaakko Hollmén
2017 Pattern Recognition  
In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data.  ...  If labels indices are considered as time indices, the problems can often be seen as equivalent.  ...  Acknowledgements This work was supported in part by the Aalto University AEF research programme http://energyefficiency.aalto.fi/en/  ... 
doi:10.1016/j.patcog.2016.09.015 fatcat:ypdjctet75aedikvnwp74hxvk4

A Survey on Multi-label Data Stream Classification

Xiulin Zheng, Peipei Li, Zhe Chu, Xuegang Hu
2019 IEEE Access  
Secondly, we identify mining constraints on classification for multi-label streaming data, and present a comprehensive study in algorithms for multi-label data stream classification.  ...  Finally, several challenges and open issues in multi-label data stream classification are discussed, which are worthwhile to be pursued by the researchers in the future.  ...  This is beneficial for classifiers to capture the correlations among labels, because each BR classifier in the chain can learn the label correlations of every former classifier in the chain except the  ... 
doi:10.1109/access.2019.2962059 fatcat:wqws3xkpmzeenatuzftjfshb2a

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Zhenwu Wang, Tielin Wang, Benting Wan, Mengjie Han
2020 Entropy  
How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels.  ...  In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned  ...  Conflicts of Interest: The authors declare no conflict of interest. Entropy 2020, 22, 1143  ... 
doi:10.3390/e22101143 pmid:33286912 fatcat:6xwh6lngingmvluhgoirtirbne

Introducing Multi-Source Domain Adaptation for Quality Control in Retail Food Packaging

Mamatha Thota, Georgios Leontidis
2020 Zenodo  
This was achieved by improving the generalization of the techniques via incorporating new loss functions and making use of multi-source datasets in order to extract domain invariant representations for  ...  The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks.  ...  Initially many shallow models were proposed in order to tackle the multi-source domain adaptation problem [6] [7] .  ... 
doi:10.5281/zenodo.4077393 fatcat:vlp3oneni5bvrfmhkltrdddwx4

Classifier Chains: A Review and Perspectives [article]

Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank
2020 arXiv   pre-print
The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems.  ...  This approach involves linking together off-the-shelf binary classifiers in a chain structure, such that class label predictions become features for other classifiers.  ...  Acknowledgements Thanks to Tomasz Kajdanowicz and Willem Waegeman who pointed out useful references and provided insightful discussion on classifier chains during the preparation of this paper.  ... 
arXiv:1912.13405v2 fatcat:nzpdycyj5fgrtp445sxfivzamm
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