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The return of AdaBoost.MH: multi-class Hamming trees [article]

Balázs Kégl
<span title="2013-12-20">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Within the framework of AdaBoost.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem to K binary one-against-all classifications  ...  In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLogitBoost, and it is significantly better than other known implementations of AdaBoost.MH  ...  Conclusion In this paper we introduced Hamming trees that optimize the multi-class edge prescribed by ADABOOST.MH without reducing the multi-class problem to K binary oneagainst-all classifications.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1312.6086v1">arXiv:1312.6086v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zy7xglnktjecjc27qkzmorz7qm">fatcat:zy7xglnktjecjc27qkzmorz7qm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200904140852/https://arxiv.org/pdf/1312.6086v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/7c/a37c1df39575fd59d8b3b4697da2de486c71ab3e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1312.6086v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Boosting products of base classifiers

Balázs Kégl, Róbert Busa-Fekete
<span title="">2009</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/v54jrmjbpzgodi4buoyaj7vrzm" style="color: black;">Proceedings of the 26th Annual International Conference on Machine Learning - ICML &#39;09</a> </i> &nbsp;
On benchmark datasets, our boosted products of decision stumps clearly outperform boosted trees, and on the MNIST dataset the algorithm achieves the second best result among no-domain-knowledge algorithms  ...  In this paper we show how to boost products of simple base learners. Similarly to trees, we call the base learner as a subroutine but in an iterative rather than recursive fashion.  ...  The goal of the ADABOOST.MH algorithm ( (Schapire & Singer, 1999) , Figure 1 ) is to return a vector-valued classifier f : X → R K with a small Hamming loss R H f (T ) , W (1) = n i=1 K =1 w (1) i, I  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1553374.1553439">doi:10.1145/1553374.1553439</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icml/KeglB09.html">dblp:conf/icml/KeglB09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ualtj6zgkfhvng2cizkxo46jd4">fatcat:ualtj6zgkfhvng2cizkxo46jd4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130419032216/http://www.machinelearning.org:80/archive/icml2009/papers/231.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/09/fa097d2ef9dafa5690f8120fb4e02957866ed0ac.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1553374.1553439"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Multi-class AdaBoost with Hypothesis Margin

Xiaobo Jin, Xinwen Hou, Cheng-Lin Liu
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jsl2pgelqja2piczru3a6nqkg4" style="color: black;">2010 20th International Conference on Pattern Recognition</a> </i> &nbsp;
Most AdaBoost algorithms for multi-class problems have to decompose the multi-class classification into multiple binary problems, like the Adaboost.MH and the LogitBoost.  ...  We discuss the upper bound of the training error about AdaBoost.HM and a previous multi-class learning algorithm AdaBoost.M1.  ...  For multi-class classification problems, most AdaBoost algorithms reduce the multiclass problem into multiple binary problems, such as AdaBoost.MH (AdaBoost with Multi-class Hamming Loss), AdaBoost.MR  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr.2010.25">doi:10.1109/icpr.2010.25</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icpr/JinHL10.html">dblp:conf/icpr/JinHL10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eej6jgelrneelnmc5naogewby4">fatcat:eej6jgelrneelnmc5naogewby4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170810230707/http://nlpr-web.ia.ac.cn/2010papers/gjhy/gh17.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5c/dc/5cdc85aa358563959230313e3be2e7a69d2181d4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr.2010.25"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

:{unav)

Robert E. Schapire, Yoram Singer
<span title="2012-12-27">2012</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4nnd7sxwzcwhetu5qkjbcdh6u" style="color: black;">Machine Learning</a> </i> &nbsp;
We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks.  ...  Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks.  ...  Description of the classes constituting the second multi-label subset of Reuters-21450.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1023/a:1007649029923">doi:10.1023/a:1007649029923</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ofe6zxzvfneohhxwj5msouvl2i">fatcat:ofe6zxzvfneohhxwj5msouvl2i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812221409/http://ccc.inaoep.mx/~villasen/bib/BoosTexter%20-%20A%20Boosting-based%20System%20for%20Text%20Classification.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/5e/375eb066d1602dc560249a90449afed00196550e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1023/a:1007649029923"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Multi-label hypothesis reuse

Sheng-Jun Huang, Yang Yu, Zhi-Hua Zhou
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;12</a> </i> &nbsp;
an estimate of the label relationship.  ...  Multi-label learning arises in many real-world tasks where an object is naturally associated with multiple concepts.  ...  They both train additive models to directly optimize multi-label losses, i.e., hamming loss for AdaBoost.MH and ranking loss for AdaBoost.MR.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2339530.2339615">doi:10.1145/2339530.2339615</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/HuangYZ12.html">dblp:conf/kdd/HuangYZ12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dpzg4fn7tjdrlaqno2yamizp2u">fatcat:dpzg4fn7tjdrlaqno2yamizp2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809091625/http://wan.poly.edu/KDD2012/docs/p525.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a9/5b/a95b2f7201302198213a387021253ec569ded471.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2339530.2339615"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Comprehensive Comparative Study of Multi-Label Classification Methods [article]

Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski, Dragi Kocev
<span title="2021-02-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Multi-label classification (MLC) has recently received increasing interest from the machine learning community.  ...  Second, the datasets cover a wide range of complexity and domains of application. The selected evaluation measures assess the predictive performance and the efficiency of the methods.  ...  Each of the LP methods should learn 2 k classes instead of 2 |L| , where k << |L|. Moreover, the resulting multi-class problems have a much better-balanced distribution of the classes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.07113v2">arXiv:2102.07113v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jtjefamw35fetjtnatmjvjl544">fatcat:jtjefamw35fetjtnatmjvjl544</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210218012402/https://arxiv.org/pdf/2102.07113v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/69/4a/694a8866663bcfadff5e4ad7571049657de2f2a3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.07113v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter [article]

Arjun Pakrashi, Brian Mac Namee
<span title="2021-03-10">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification  ...  This work proposes a multi-label version of KFHE, ML-KFHE, demonstrating the effectiveness of the KFHE method on multi-label datasets.  ...  The random forest of predictive clustering trees (RF-PCT) [12] is a multi-label method which uses predictive clustering trees (PCT) [1] as the tree algorithm in a random forest [20] framework.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.10552v3">arXiv:1904.10552v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eulbzmgic5h2tnsicwngiv7cnm">fatcat:eulbzmgic5h2tnsicwngiv7cnm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200903060241/https://arxiv.org/pdf/1904.10552v2.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f6/67/f6672675f04f5fb6f75e701080dca67ebf6e4588.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.10552v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Boosting Applied to Word Sense Disambiguation [article]

Gerard Escudero, Lluis Marquez, German Rigau
<span title="2000-07-07">2000</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem.  ...  In order to make boosting practical for a real learning domain of thousands of words, several ways of accelerating the algorithm by reducing the feature space are studied.  ...  factor (chosen so that Dt+1 will be a distribution) end-for return the combined hypothesis: f (x, l) = T t=1 ht(x, l) end AdaBoost.MH Fig. 1.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/cs/0007010v1">arXiv:cs/0007010v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/piawv3o6ajcprj32ulrzribn6m">fatcat:piawv3o6ajcprj32ulrzribn6m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171008194748/https://core.ac.uk/download/pdf/2417450.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c0/14/c014aef4dd147dda7f4bb194ec8f251351ee44b1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/cs/0007010v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Boosting multi-label hierarchical text categorization

Andrea Esuli, Tiziano Fagni, Fabrizio Sebastiani
<span title="2008-02-28">2008</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qkuxm4jkpnclphpwzqgsg7qnae" style="color: black;">Information retrieval (Boston)</a> </i> &nbsp;
In this paper we propose TREEBOOST.MH, a multi-label HTC algorithm consisting of a hierarchical variant of ADABOOST.MH, a very well-known member of the family of "boosting" learning algorithms.  ...  tree structure.  ...  Acknowledgements This work has been partially supported by Project ''Networked Peers for Business'' (NeP4B), funded by the Italian Ministry of University and Research (MIUR) under the ''Fondo per gli Investimenti  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10791-008-9047-y">doi:10.1007/s10791-008-9047-y</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rbgapb7shvgllnhgnzsabvnv3a">fatcat:rbgapb7shvgllnhgnzsabvnv3a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20080424174205/http://nmis.isti.cnr.it/sebastiani/Publications/IRJ08.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/49/fe/49fe27b34f2d8f7f457bb0fa2df75a8b5c2a1650.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10791-008-9047-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

MP-Boost: A Multiple-Pivot Boosting Algorithm and Its Application to Text Categorization [chapter]

Andrea Esuli, Tiziano Fagni, Fabrizio Sebastiani
<span title="">2006</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
AdaBoost.MH is a popular supervised learning algorithm for building multi-label (aka n-of-m) text classifiers.  ...  We present the results of experiments showing that MP-Boost is much more effective than AdaBoost.MH.  ...  tree of depth one, i.e. consisting of a root node and two or more leaf nodes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11880561_1">doi:10.1007/11880561_1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4g4a4eo6l5bldp2gj7vlxku52q">fatcat:4g4a4eo6l5bldp2gj7vlxku52q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170921211445/http://puma.isti.cnr.it/rmydownload.php?filename=cnr.isti/cnr.isti/2006-TR-07/2006-TR-07.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f1/dc/f1dcb9ee06069cdb8d574881f8c88e3fdd5bb032.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11880561_1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets

Juan Luis Olmo, Cristóbal Romero, Eva Gibaja, Sebastián Ventura
<span title="">2015</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wjdkfhqywrdefeqoyzjz3os76i" style="color: black;">International Journal of Computational Intelligence Systems</a> </i> &nbsp;
The subset of algorithms along with the meta-features extracted from the training data are used to generate a multi-label data set.  ...  This paper proposes a new meta-learning framework for educational domains based on the use of multi-label learning for selecting the best classification algorithms in order to predict students' performance  ...  It provides the percentage of nodes that link different classes in a minimum spanning tree constructed over the data set, counting the number of points incident on an edge going across the two classes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/18756891.2015.1113748">doi:10.1080/18756891.2015.1113748</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gaeplbopqvhtjljijwd7kggk3y">fatcat:gaeplbopqvhtjljijwd7kggk3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200404023801/https://download.atlantis-press.com/article/25868655.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/08/b3/08b354c77a98b1a950751c8eec6b93b2495d4103.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/18756891.2015.1113748"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Traffic sign recognition system with β -correction

Sergio Escalera, Oriol Pujol, Petia Radeva
<span title="2008-06-06">2008</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lvjqpr55r5fcfpfbeu7qp2jwgm" style="color: black;">Machine Vision and Applications</a> </i> &nbsp;
Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.  ...  In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC).  ...  For this reason, the comparison with multi-class Adaboost.MH has been omitted from the set of experiments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00138-008-0145-z">doi:10.1007/s00138-008-0145-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l6ois3vr6bdpfficz4dz3v62oi">fatcat:l6ois3vr6bdpfficz4dz3v62oi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20091225075142/http://www.maia.ub.es/%7Esergio/files/MVA08.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9c/87/9c871d2de7ba39df86962d6ffdc1773f2d4985d9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00138-008-0145-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Multi-Label Classification Approach Based on Correlations Among Labels

Raed Alazaidah, Fadi Thabtah, Qasem Al-Radaideh
<span title="">2015</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
Multi label classification is concerned with learning from a set of instances that are associated with a set of labels, that is, an instance could be associated with multiple labels at the same time.  ...  The output of the approach is multi-labels rules. The approach also tries to get benefit from positive correlations among labels using predictive Apriori algorithm.  ...  ACKNOWLEDGMENT Raed Alazaidah thanks all the professors in faculty of information technology in Philadelphia university, especially prof. Said Ghoul. And deep thanks to my best friend Naela Alsalman.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2015.060208">doi:10.14569/ijacsa.2015.060208</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4vkmf527fbcxjbjgnawsi3zene">fatcat:4vkmf527fbcxjbjgnawsi3zene</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150630011119/http://thesai.org/Downloads/Volume6No2/Paper_8-A_Multi-Label_Classification_Approach_Based_on_Correlations_Among_Labels.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/16/af/16af4e76340b5b78dd28e14ace19d2623c27be5f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2015.060208"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

HYBRID DECISION TREE ARCHITECTURE UTILIZING LOCAL SVMs FOR EFFICIENT MULTI-LABEL LEARNING

DEJAN GJORGJEVIKJ, GJORGJI MADJAROV, SAŠO DŽEROSKI
<span title="">2013</span> <i title="World Scientific Pub Co Pte Lt"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ctczi3cdmjfarjhjbdobmuwpkm" style="color: black;">International journal of pattern recognition and artificial intelligence</a> </i> &nbsp;
as a result of the integrated decision tree.  ...  We propose a hybrid decision tree architecture, where the leaves do not give multi-label predictions directly, but rather utilize local SVM-based classifiers giving multi-label predictions.  ...  AdaBoost.MH and Ad-aBoost.MR 8 are two extensions of AdaBoost for multi-label data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1142/s021800141351004x">doi:10.1142/s021800141351004x</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hkovaznizbabza5cqrmvpiklve">fatcat:hkovaznizbabza5cqrmvpiklve</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706020802/http://dejan.gjorgjevikj.com/papers/MLSVMDT.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1a/c3/1ac3a3b0f24f6b30c746ff61acfe04e6c0ffaf1b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1142/s021800141351004x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> worldscientific.com </button> </a>

Multi-label Classification without the Multi-label Cost [chapter]

Xiatian Zhang, Quan Yuan, Shiwan Zhao, Wei Fan, Wentao Zheng, Zhong Wang
<span title="2010-04-29">2010</span> <i title="Society for Industrial and Applied Mathematics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/viwc2ys5x5a47ogpdlftfzj5fm" style="color: black;">Proceedings of the 2010 SIAM International Conference on Data Mining</a> </i> &nbsp;
Importantly, we demonstrate that the training complexity is independent from the number of class labels, a significant overhead for many state-of-the-art multi-label methods.  ...  We formally analyze the learning risk of Random Decision Tree (RDT) and derive that the upper bound of risk is stable and lower bound decreases as the number of trees increases.  ...  Unauthorized reproduction of this article is prohibited.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/1.9781611972801.68">doi:10.1137/1.9781611972801.68</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sdm/ZhangYZFZW10.html">dblp:conf/sdm/ZhangYZFZW10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/peugf5d7irh4xpofe4snecue2i">fatcat:peugf5d7irh4xpofe4snecue2i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20121018163039/http://www.siam.org/proceedings/datamining/2010/dm10_068_zhangx.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/af/62/af623a08c549f205b8d2e287122c96bf5117f499.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/1.9781611972801.68"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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