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Bayes-Optimal Hierarchical Multilabel Classification

Wei Bi, Jame T. Kwok
2015 IEEE Transactions on Knowledge and Data Engineering  
We utilize a hierarchical set of multilabel classifiers to predict genres and subgenres and rely on a voting scheme to predict labels across datasets.  ...  This paper summarizes our contribution (team DBIS) to the Acous-ticBrainz Genre Task: Content-based music genre recognition from multiple sources as part of MediaEval 2017.  ...  Multilabel classification.  ... 
doi:10.1109/tkde.2015.2441707 fatcat:q7wzvifruzhztf5agozmjyez7m

Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations

Guangxiang Zhao, Jingjing Xu, Qi Zeng, Xuancheng Ren, Xu Sun
2019 Proceedings of the 2019 Conference of the North  
This paper explores a new natural language processing task, review-driven multi-label music style classification.  ...  To tackle this problem, we propose a novel deep learning approach to automatically learn and exploit style correlations.  ...  Acknowledgments We thank all reviewers for providing the thoughtful and constructive suggestions. This work was supported in part by National Natural Science Foundation of China (No. 61673028).  ... 
doi:10.18653/v1/n19-1296 dblp:conf/naacl/ZhaoXZR019 fatcat:754cbk2cozck7lhzcc3o2ijyfa

Multilabel classification via calibrated label ranking

Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker
2008 Machine Learning  
Empirical results in the area of text categorization, image classification and gene analysis underscore the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.  ...  In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable  ...  We would like to thank the anonymous reviewers and the editor for their helpful suggestions. We also thank Janez Demšar for an interesting discussion on significance tests.  ... 
doi:10.1007/s10994-008-5064-8 fatcat:ehzskcl6gjfhphk3kd2mkm57cm

An Association-Based Approach To Genre Classification In Music

Tom Arjannikov, John Z. Zhang
2014 Zenodo  
We would like to look for these patterns and use them for music genre classification. Kuo et al.  ...  Moreover, sometimes it votes for all genres equally, where MLR becomes equal to the number of genres.  ... 
doi:10.5281/zenodo.1415785 fatcat:2d7txwngqjhu5ohiob54za6vwq

Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network

Kumar Govindaswamy, Bharathiar University, Shriram Ragunathan, Bharathiar University
2021 International Journal of Intelligent Engineering and Systems  
In this study, the Hierarchical Attention Neural Network (HANN) is proposed for genre classification of movies based on the social media called Twitter data as input.  ...  Twitter data related to the Telugu and English movies are collected and applied to HANN for movie's genre classification. IMDB data are used to evaluate the performance of the proposed HANN method.  ...  The supervision and project administration, have been done by 2 nd author.  ... 
doi:10.22266/ijies2021.0228.06 fatcat:m7ckrx5enfdflk4gi75fzewjwa

Hierarchical Ensemble Methods for Protein Function Prediction

Giorgio Valentini
2014 ISRN Bioinformatics  
Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple  ...  In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines.  ...  Acknowledgements The author thanks the reviewers for their comments and suggestions and acknowledges partial support from the PRIN project "Automi e linguaggi formali: aspetti matematici e applicativi"  ... 
doi:10.1155/2014/901419 pmid:25937954 pmcid:PMC4393075 fatcat:i6w56fpbqnekjozm2kdpik635e

Multi-label classification of music by emotion

Konstantinos Trohidis, Grigorios Tsoumakas, George Kalliris, Ioannis Vlahavas
2011 EURASIP Journal on Audio, Speech, and Music Processing  
Single-label classification and regression cannot model this multiplicity.  ...  Results show that multi-label modeling is successful and provide interesting insights into the predictive quality of the algorithms and features.  ...  Multilabel classification allows for a natural modeling of this issue.  ... 
doi:10.1186/1687-4722-2011-426793 fatcat:4olt4c7hbfdmffmm5hnc5hmdru

A Survey of Evaluation in Music Genre Recognition [chapter]

Bob L. Sturm
2014 Lecture Notes in Computer Science  
Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic data, and other modalities.  ...  This paper compiles a bibliography of work in MGR, and analyzes three aspects of evaluation: experimental designs, datasets, and figures of merit.  ...  This work is supported in part by: Independent Postdoc Grant 11-105218 from Det Frie Forskningsråd; and the Danish Council for Strategic Research of the Danish Agency for Science Technology and Innovation  ... 
doi:10.1007/978-3-319-12093-5_2 fatcat:uekcticb4jfmlkvcxs7mehcgoi

Recognition of Instrument Timbres in Real Polytimbral Audio Recordings [chapter]

Elżbieta Kubera, Alicja Wieczorkowska, Zbigniew Raś, Magdalena Skrzypiec
2010 Lecture Notes in Computer Science  
This paper tests recognition of instruments in real recordings, using a recognition system which has multilabel and hierarchical structure. Random forest classifiers were applied to build the system.  ...  The obtained results are shown and discussed in the paper.  ...  This project was partially supported by the Research Center of PJIIT, supported by the Polish National Committee for Scientific Research (KBN) and also by the National Science Foundation under Grant Number  ... 
doi:10.1007/978-3-642-15883-4_7 fatcat:gfiqdrvrkbgqri5ulcsj3ifssa

TwistBytes – Hierarchical Classification at GermEval 2019: walking the fine line (of recall and precision) [article]

Fernando Benites
2019 arXiv   pre-print
We achieved first place in the hierarchical subtask B and second place on the root node, flat classification subtask A.  ...  For the hierarchical classification, we used a local approach, which was more light-weighted but was similar to the one used in subtask A.  ...  Acknowledgements We thank Mark Cieliebak and Pius von Däniken for the fruitful discussions. We also thank the organizers of the GermEval 2019 Task 1.  ... 
arXiv:1908.06493v1 fatcat:osroryq7wfaklamg6efvac55im

Deep Learning and Music Adversaries

Corey Kereliuk, Bob L. Sturm, Jan Larsen
2015 IEEE transactions on multimedia  
For two different train-test partitionings of two benchmark datasets, and two different deep architectures, we find that this adversary is very effective in defeating the resulting systems.  ...  We find the convolutional networks are more robust, however, compared with systems based on a majority vote over individually classified audio frames.  ...  ACKNOWLEDGMENTS CK and JL were supported in part by the Danish Council for Strategic Research of the Danish Agency for Science Technology and Innovation under the CoSound project, case number 11-115328  ... 
doi:10.1109/tmm.2015.2478068 fatcat:7zpv74zsivhn7nicp53jwprm7a

Deep Learning and Music Adversaries [article]

Corey Kereliuk and Bob L. Sturm and Jan Larsen
2015 arXiv   pre-print
For two different train-test partitionings of two benchmark datasets, and two different deep architectures, we find that this adversary is very effective in defeating the resulting systems.  ...  We find the convolutional networks are more robust, however, compared with systems based on a majority vote over individually classified audio frames.  ...  ACKNOWLEDGMENTS CK and JL were supported in part by the Danish Council for Strategic Research of the Danish Agency for Science Technology and Innovation under the CoSound project, case number 11-115328  ... 
arXiv:1507.04761v1 fatcat:tcsqmg2kkvhl7bn2phzagqcx7m

Embedding based Link Prediction for Knowledge Graph Completion

Russa Biswas
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
Knowledge Graphs (KGs) have recently gained attention for representing knowledge about a particular domain.  ...  However, the information present in the KGs are sparse and are often incomplete. Predicting the missing links between the entities is necessary to overcome this issue.  ...  Harald Sack and Dr. Mehwish Alam.  ... 
doi:10.1145/3340531.3418512 dblp:conf/cikm/Biswas20 fatcat:bgsrwzlh6bcbzeydijfc6ek7bu

A Survey on Visual Content-Based Video Indexing and Retrieval

Weiming Hu, Nianhua Xie, Li Li, Xianglin Zeng, S. Maybank
2011 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot  ...  retrieval including query interfaces, similarity measure and relevance feedback, and video browsing.  ...  The C4.5 decision tree is used to build the classifier for genre labeling. Yuan et al. [240] present an automatic video genre classification method based on a hierarchical ontology of video genres.  ... 
doi:10.1109/tsmcc.2011.2109710 fatcat:qtenus4htffcfbyuiwidgjojku

An extensive experimental comparison of methods for multi-label learning

Gjorgji Madjarov, Dragi Kocev, Dejan Gjorgjevikj, Sašo Džeroski
2012 Pattern Recognition  
The results of the analysis show that for multi-label classification the best performing methods overall are random forests of predictive clustering trees (RF-PCT) and hierarchy of multi-label classifiers  ...  We analyze the results from the experiments using Friedman and Nemenyi tests for assessing the statistical significance of differences in performance.  ...  CLR is considered a combination of multi-label classification and ranking. The Quick Weighted voting method for multi-class classification, proposed by Park et al.  ... 
doi:10.1016/j.patcog.2012.03.004 fatcat:wcjofxautffxlajqwh64xwwd3y
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