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Multi-label collective classification in multi-attribute multi-relational network data

Priyesh Vijayan, Shivashankar Subramanian, Balaraman Ravindran
2014 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)  
Motivated by this, we propose a learning technique for multi-label collective classification using multiple attribute views on multi-relational network data which captures complex label correlations within  ...  the nodes in an iterative procedure.  ...  ) based classification in an iterative procedure.  ... 
doi:10.1109/asonam.2014.6921634 dblp:conf/asunam/VijayanSR14 fatcat:crridxl6xfhndcbl5qkr2buxfa

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

Arjun Pakrashi, Brian Mac Namee
2021 arXiv   pre-print
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  ...  Also, the ML-KFHE-HOMER variant was found to perform consistently and significantly better than existing multi-label methods including existing approaches based on ensembles.  ...  KFHE-HOMER is an ensemble method for multi-label classification that combines the HOMER [25] approach to multi-label classification with the Kalman Filter-based Heuristic Ensemble (KFHE) [16] , a recent  ... 
arXiv:1904.10552v3 fatcat:eulbzmgic5h2tnsicwngiv7cnm

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.  ...  In this survey, we provide a comprehensive review of existing multi-label streams mining algorithms and categorize these methods based on different perspectives, which mainly focus on the multi-label data  ...  Ensemble is an important mechanism in multi-label data stream classification.  ... 
doi:10.1109/access.2019.2962059 fatcat:wqws3xkpmzeenatuzftjfshb2a

Multi-label ECG Signal Classification Based on Ensemble Classifier

Zhanquan Sun, Chaoli Wang, Yangyang Zhao, Chao Yan
2020 IEEE Access  
It provides a feasible analysis method for multi-label ECG signal automatic classification. INDEX TERMS Electrocardiogram, multi-label classification, ensemble classification, mutual information.  ...  To resolve the multi-label ECG signal classification problems, we propose a novel ensemble multi-label classification model in this paper.  ...  For improving the classification performance, an ensemble multi-label classifier is proposed to realize high performance ECG signal classification.  ... 
doi:10.1109/access.2020.3004908 fatcat:bjl5ugj7gbc3lhvs7tr5umlrn4

Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis

Mingxia Liu, Daoqiang Zhang, Ehsan Adeli, Dinggang Shen
2016 IEEE Transactions on Biomedical Engineering  
In this paper, we propose an inherent structure based multi-view leaning (ISML) method using multiple templates for AD/MCI classification.  ...  Finally, we learn an ensemble of view-specific support vector machine (SVM) classifiers based on their respectively selected features in each view, and fuse their results to draw the final decision.  ...  SVM-based Ensemble Classification To better take advantage of multi-view feature representation generated from different templates, we further propose an SVM-based ensemble classification approach.  ... 
doi:10.1109/tbme.2015.2496233 pmid:26540666 pmcid:PMC4851920 fatcat:ugcmbsip45glrorcfo7fuhsawa

A Multiclass-based Classification Strategy for Rhetorical Sentence Categorization from Scientific Papers

Dwi H. Widyantoro, Masayu L. Khodra, Bambang Riyanto, E. Aminudin Aziz
2013 Journal of ICT Research and Applications  
This paper presents a multi terms of classification of r behind this approach is based on an observation that no single classifier is the best performer for classifyi our approach learns which classifiers  ...  the multi improve the classification performance over multi  ...  research was partially supported by ITB Research and Innovation Grant under contract# 0277/I1.C07/PL/2012 Any opinions, findings, or conclusions are those of the authors, and do not necessarily reflect the views  ... 
doi:10.5614/itbj.ict.res.appl.2013.7.3.5 fatcat:dbdmmdz2oncpncv3tbhjxdfz3i

Empirical Study of Multi-label Classification Methods for Image Annotation and Retrieval

Gulisong Nasierding, Abbas Z. Kouzani
2010 2010 International Conference on Digital Image Computing: Techniques and Applications  
This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications.  ...  This provides an indication of the suitability of different multi-label classification methods for automatic image annotation under different problem settings.  ...  AIA can be grouped into statistical model and classification based approaches [3, 5, 6] , and the classification based approaches can be further divided into single-label and multi-label classification  ... 
doi:10.1109/dicta.2010.113 dblp:conf/dicta/NasierdingK10 fatcat:6bpjdbth35adxknxauedb2thoe

Inherent Structure-Guided Multi-view Learning for Alzheimer's Disease and Mild Cognitive Impairment Classification [chapter]

Mingxia Liu, Daoqiang Zhang, Dinggang Shen
2015 Lecture Notes in Computer Science  
In this paper, we propose an inherent structure-guided multi-view leaning (ISML) method for AD/MCI classification.  ...  Finally, we learn multiple SVM classifiers based on the selected features, and fuse them together by an ensemble classification method.  ...  supported by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, and AG042599), the National Natural Science Foundation of China (Nos. 61422204, 61473149) , the Jiangsu Natural Science Foundation for  ... 
doi:10.1007/978-3-319-24888-2_36 pmid:27088137 pmcid:PMC4830487 fatcat:mpy6unynnfhqde6ts4kudvshxq

A Triple-Random Ensemble Classification Method for Mining Multi-label Data

Gulisong Nasierding, Abbas Z. Kouzani, Grigorios Tsoumakas
2010 2010 IEEE International Conference on Data Mining Workshops  
This paper presents a triple-random ensemble learning method for handling multi-label classification problems.  ...  The proposed method integrates and develops the concepts of random subspace, bagging and random k-labelsets ensemble learning methods to form an approach to classify multi-label data.  ...  This paper introduces a novel PT based triplerandom ensemble multi-label classification (TREMLC) framework to handle multi-label problems from various domains.  ... 
doi:10.1109/icdmw.2010.139 dblp:conf/icdm/NasierdingKT10 fatcat:abtnzghjjne7bhnhhzwm4uboc4

Study and Analysis of Multi-Label Classification Methods in Data Mining

Shubhangi R., Suraj R.
2017 International Journal of Computer Applications  
In algorithm adaptation approaches, algorithms are adapted in order to perform the multi-label classification directly.  ...  Basically there are two different techniques for handling the multi-label classification problem such as techniques of problem transformation and techniques of algorithm adaptation.  ...  BR is most simple approach for multi-label classification, but this approach completely rejects the dependencies between multiple labels.  ... 
doi:10.5120/ijca2017913035 fatcat:atxgkfiopvbqxjafj4xtgxhlwm

Multi-label ensemble based on variable pairwise constraint projection

Ping Li, Hong Li, Min Wu
2013 Information Sciences  
To achieve these goals, this paper presents a novel multi-label classification framework named Variable Pairwise Constraint projection for Multi-label Ensemble (VPCME).  ...  Multi-label classification has attracted an increasing amount of attention in recent years. To this end, many algorithms have been developed to classify multi-label data in an effective manner.  ...  This work was supported in part by the National Science Foundation for Outstanding Young Scientists of China (60425310) and the Fundamental Research Funds for the Central Universities (2012FZA5017).  ... 
doi:10.1016/j.ins.2012.07.066 fatcat:uob6q5joizcp7azxdynmcw5xou

Tensor Ensemble Learning for Multidimensional Data [article]

Ilia Kisil, Ahmad Moniri, Danilo P. Mandic
2018 arXiv   pre-print
To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor-valued data is introduced.  ...  The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of ensemble learning.  ...  Consider an ensemble of N independent classifiers, C = {C 1 , . . . , C N }, employed for a binary classification problem based on a dataset D : {(x 1 , y 1 ), . . . , (x M , y M )}, where y m ∈ {0, 1}  ... 
arXiv:1812.06888v1 fatcat:5sln4in6djfrnn23qfaollgx2a

Semi-Supervised learning with Collaborative Bagged Multi-label K-Nearest-Neighbors

Nesma Settouti, Khalida Douibi, Mohammed El Amine Bechar, Mostafa El Habib Daho, Meryem Saidi
2019 Open Computer Science  
Over the last few years, Multi-label classification has received significant attention from researchers to solve many issues in many fields.  ...  The manual annotation of available datasets is time-consuming and need a huge effort from the expert, especially for Multi-label applications in which each example of learning is associated with many labels  ...  In [39] , the authors proposed an ensemble Multi-label classifier based on Label Powerset strategy to resolve the multi-disease risk prediction based on physical examination records.  ... 
doi:10.1515/comp-2019-0017 fatcat:24kgnww2mnattcnbjl25f5osda

Multiple-View Active Learning for Environmental Sound Classification

Yan Zhang, Danjv Lv, Yili Zhao
2016 International Journal of Online Engineering (iJOE)  
The experimental results show that multi-view active learning can effectively improve the performance of classification for environmental sound data, and MV-EPS method outperforms the MV-SDS.  ...  case of few labeled data.  ...  Ensemble technology applied into the SVSL, multi-classifier is used to train for sampling the disagreement points with single-view.  ... 
doi:10.3991/ijoe.v12i12.6458 fatcat:yp6zfwf6gna25famcv4ersr3ue

A Multi-path Strategy for Hierarchical Ensemble Classification [chapter]

Esra'a Alshdaifat, Frans Coenen, Keith Dures
2014 Lecture Notes in Computer Science  
A solution to the multi-class classification problem is proposed founded on the concept of an ensemble of classifiers arranged in a hierarchical binary tree formation.  ...  To address this issue a multi-path strategy is investigated based on the idea of using Classification Association Rule Miners at individual nodes.  ...  A more recent approach for arranging the base classifiers within an ensemble model involves the creation and utilisation of a hierarchy of classifiers [18, 10, 32, 4, 19, 24] .  ... 
doi:10.1007/978-3-319-08979-9_16 fatcat:4trpxyp7x5hdbafmwubuupdi2y
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