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MEKA: A Multi-label/Multi-target Extension to WEKA

Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes
2016 Journal of machine learning research  
It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.  ...  Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning.  ...  It also includes methods for incremental, multi-target, and semi-supervised learning, and is easy to use and extend.  ... 
dblp:journals/jmlr/ReadRPH16 fatcat:kq3szfnlmbgffi36vmrm6htueu

A Survey on Multi-label Classification for Images

Radhika Devkar, Sankirti Shiravale
2017 International Journal of Computer Applications  
Finally, paper is concluded towards challenges in multi-label classification for images for future research.  ...  The area of an image multi-label classification is increase continuously in last few years, in machine learning and computer vision.  ...  [9] , semi-supervised multi-label classification for image use manifold regularized multitask learning (MRMTL) based algorithm. Xiangyang Xue et al.  ... 
doi:10.5120/ijca2017913398 fatcat:ogsxomnv2fet3pmio5tbxym3fe

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  
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  ...  Experiments on ten real-world Multi-label datasets show the effectiveness of CobMLKNN algorithm to improve the performance of MLKNN to learn from a small number of labeled samples by exploiting unlabeled  ...  Semi-supervised learning was highly used by researchers to reduce labeling efforts and produce more instances for the training process of the algorithms, especially, for Multi-label classification.  ... 
doi:10.1515/comp-2019-0017 fatcat:24kgnww2mnattcnbjl25f5osda

Towards Multi Label Text Classification through Label Propagation

Shweta C, Maya Ingle, Parag Kulkarni
2012 International Journal of Advanced Computer Science and Applications  
Through our paper we are proposing a novel label propagation approach based on semi supervised learning for Multi Label Text Classification.  ...  We are using semi supervised learning technique for effective utilization of labeled and unlabeled data for classification.  ...  Generally supervised methods from machine learning are mainly used for realization of multi label text classification.  ... 
doi:10.14569/ijacsa.2012.030607 fatcat:75ae7hgvybdfroyx2gak4ndzci

Analysis of Semi Supervised Learning Methods towards Multi Label Text Classification

S. C.Dharmadhikari, Maya Ingle, Parag Kulkarni
2012 International Journal of Computer Applications  
But as it needs labeled data for classification all the time , semi supervised methods are now a day getting popular in the MLTC domain.  ...  document datasets , their representation in conjunction with smoothness and manifold assumptions in semi supervised learning may give more relevant classification results.  ...  It was very popular attempt to introduce semi supervised learning for text document classification.  ... 
doi:10.5120/5775-8026 fatcat:oqkzmctw2zao7hzrgjkt5ut2j4

A Novel Multi label Text Classification Model using Semi supervised learning

Shweta C Dharmadhikari
2012 International Journal of Data Mining & Knowledge Management Process  
We are proposing a new multi label text classification model for assigning more relevant set of categories to every input text document.  ...  Through this paper a classification model for ATC in multi-label domain is discussed.  ...  The most traditional approach towards multi-label learning decomposes the classification task into multiple independent binary classification tasks, one for each category.  ... 
doi:10.5121/ijdkp.2012.2402 fatcat:hhn3aa63zjdovnwgbvy25v236a

Learning From Semi-Supervised Weak-Label Data

Hao-Chen Dong, Yu-Feng Li, Zhi-Hua Zhou
We call this kind of problem as "semi-supervised weak-label learning" problem. In this work we propose the SSWL (Semi-Supervised Weak-Label) method to address this problem.  ...  Multi-label learning deals with data objects associated with multiple labels simultaneously.  ...  For each label, a number of existing binary semi-supervised learning algorithms, such as label propagation (Zhu and Goldberg 2009) and semi-supervised SVMs (Chapelle, Schölkopf, and Zien 2006) , can  ... 
doi:10.1609/aaai.v32i1.11762 fatcat:s5lp6ovggngbngalscuwmmlsnq

Optimising Agile Social Media Analysis

Thomas Kober, David Weir
2015 Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis  
We evaluate several semi-supervised learning algorithms in conjunction with a Naïve Bayes model, and show how these modifications can improve the performance of bespoke classifiers for a variety of tasks  ...  Agile social media analysis involves building bespoke, one-off classification pipelines tailored to the analysis of specific datasets.  ...  Acknowledgments We thank Jeremy Reffin, the TAG lab team and our anonymous reviewers for their helpful comments.  ... 
doi:10.18653/v1/w15-2906 dblp:conf/wassa/KoberW15 fatcat:xibncyukirgvnkyvpfabo4wsva

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

David Charte, Francisco Charte, Salvador García, Francisco Herrera
2018 Progress in Artificial Intelligence  
Within supervised learning, most studies and research are focused on well known standard tasks, such as binary classification, multiclass classification and regression with one dependent variable.  ...  This field is subdivided into multiple areas, among which the best known are supervised learning (e.g. classification and regression) and unsupervised learning (e.g. clustering and association rules).  ...  Transformation methods for ML classification [118] are diverse: Binary Relevance trains separate binary classifiers for each label.  ... 
doi:10.1007/s13748-018-00167-7 fatcat:lqg23f33hbfu3heht2swg7k2hy

Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification

Hakan Cevikalp, Burak Benligiray, Ömer Nezih Gerek, Hasan Saribas
2019 Computer Vision and Pattern Recognition  
The proposed method allows for learning from both labeled and unlabeled data in a semi-supervised learning setting.  ...  In this paper, we propose a robust method for semisupervised training of deep neural networks for multi-label image classification.  ...  Acknowledgments: The authors would like to thank NVIDIA for Tesla K40 GPU donation used in this study.  ... 
dblp:conf/cvpr/CevikalpBGS19 fatcat:fgavi3hyqvhmjc7bqs6rt7e2ce

Regularized Boost for Semi-supervised Ranking [chapter]

Zhigao Miao, Juan Wang, Aimin Zhou, Ke Tang
2015 Proceedings in Adaptation, Learning and Optimization  
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data.  ...  Several boosting algorithms have been extended to semi-supervised learning with various strategies.  ...  Conclusions We have proposed a local smoothness regularizer for semi-supervising boosting learning and demonstrated its effectiveness on different types of data sets.  ... 
doi:10.1007/978-3-319-13359-1_49 fatcat:4mafb7jwnzcbtoz4an5hrytkdq

Ontology-based multi-label classification of economic articles

Sergeja Vogrincic, Zoran Bosnic
2011 Computer Science and Information Systems  
Since the documents can be annotated with multiple keywords (labels), we approach this task by applying and evaluating multi-label classification methods of supervised machine learning.  ...  A good alternative to these approaches is also single-class naive Bayes classifiers coupled with the binary relevance transformation approach.  ...  relevance (BR) which learns binary classifiers, one for each different label in .  ... 
doi:10.2298/csis100420034v fatcat:2zh4hehiojcoxaewkkqaoajxwq

Semi-supervised Multi-label Classification [chapter]

Yuhong Guo, Dale Schuurmans
2012 Lecture Notes in Computer Science  
In this paper, we present a new semi-supervised multi-label learning method that combines large-margin multi-label classification with unsupervised subspace learning.  ...  Although it is important to consider semi-supervised methods for multi-label learning, as it is in other learning scenarios, surprisingly, few proposals have been investigated for this particular problem  ...  Here we exploit recent results for semi-supervised convex subspace learning, which we adapt to large-margin multi-label classification.  ... 
doi:10.1007/978-3-642-33486-3_23 fatcat:4v5tkk27wbfo3okgmghlvlrism

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation [article]

Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
2015 arXiv   pre-print
In this architecture, labels associated with an image are identified by classification network, and binary segmentation is subsequently performed for each identified label in segmentation network.  ...  Contrary to existing approaches posing semantic segmentation as a single task of region-based classification, our algorithm decouples classification and segmentation, and learns a separate network for  ...  Specifically, this is because we identify the relevant labels using classification network and perform binary segmentation for each of the labels, where the number of output channels in segmentation network  ... 
arXiv:1506.04924v2 fatcat:kdljwoets5h53jes3iwph75ypy

Multi-label semi-supervised classification through optimum-path forest

Willian P. Amorim, Alexandre X. Falcão, João P. Papa
2018 Information Sciences  
Recently, we proposed a semi-supervised learning method based on optimum connectivity for singlelabel classification.  ...  In this work, we extend it for multi-label classification with considerable effectiveness gain.  ...  semi-supervised learning methods.  ... 
doi:10.1016/j.ins.2018.06.067 fatcat:v735h75v6rgsbmnxla4r52v4xy
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