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Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

Awder Mohammed Ahmed, Duhok Polytechnic University, Duhok, Kurdistan Region-IRAQ, Adnan Mohsin Abdulazeez, Duhok Polytechnic University, Duhok, Kurdistan Region-IRAQ
2021 Journal of Soft Computing and Data Mining  
There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes.  ...  Multi-label classification addresses the issues that more than one class label assigns to each instance.  ...  Acknowledgment The authors would like to acknowledge Duhok Polytechnic University/ Technical College of Informatics-Akre.  ... 
doi:10.30880/jscdm.2021.02.02.006 fatcat:biacmvnfmfafhanblgijwki2ta

Swarm Intelligence-Based Feature Selection for Multi-Label Classification: A Review

Adnan Mohsin Abdulazeez, Dathar A. Hasan, Awder Mohammed Ahmed, Omar S. Kareem
2021 Asian Journal of Research in Computer Science  
Multi-label classification is the process of specifying more than one class label for each instance.  ...  The high-dimensional data in various multi-label classification tasks have a direct impact on reducing the efficiency of traditional multi-label classifiers.  ...  These measures use the margin of the instance to granulate all instances under various labels.  ... 
doi:10.9734/ajrcos/2021/v9i430230 fatcat:mmkvev4hmrbrdl2eg6iwzufo7u

A Feature Selection Method for Multivariate Performance Measures

Qi Mao, Ivor Wai-Hung Tsang
2013 IEEE Transactions on Pattern Analysis and Machine Intelligence  
In addition, we adapt the proposed method to optimize multivariate measures for multiple instance learning problems.  ...  In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data.  ...  As shown in [8] , optimizing the learning model subject to the specific multivariate performance measures can boost the corresponding performance.  ... 
doi:10.1109/tpami.2012.266 pmid:23868769 fatcat:flbthunz4vdndot5swzqlnvbfm

Feature Selection using MultiObjective Grey Wolf Optimization Algorithm

Deepak Gupta, Nimish Verma, Mayank Sehgal, Nitesh
2019 Zenodo  
Then machine learning models like KNN, random forest and logistic regression are used to obtain the accuracy results and comparison of the results is performed.  ...  Multi Objective Grey wolf Optimization is one a meta-heuristic technique.  ...  When there are more than one objective to be optimized then it is called a multi-objective problem. In the initial years, numerous monoobjective optimizers were converted to multi-objective ones.  ... 
doi:10.5281/zenodo.4743482 fatcat:vqkuridbw5d3bla5t2wrqd2f2a

Multi-Target Prediction: A Unifying View on Problems and Methods [article]

Willem Waegeman, Krzysztof Dembczynski, Eyke Huellermeier
2018 arXiv   pre-print
Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label  ...  classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion.  ...  Multi-label classification, multivariate regression, and multi-task learning are the most well-known subfields of machine learning that can be mentioned as examples.  ... 
arXiv:1809.02352v1 fatcat:xzpeainysbh5lg73buinh6c2pm

Optimizing Machine Learning Models using Multiobjective Grasshopper Optimization Algorithm

Ashish Sharma, Deepak Gupta, Nimish Verma, Mayank Sehgal, Nitesh
2019 Zenodo  
It can be applied in numerous domains due to its impressive characteristics like easy to use, scalable, flexible and better performance than classic methods in real problems.  ...  Multi Objective Grasshopper Optimization Algorithm is a re- cent meta-heuristic swarm intelligence algorithm developed by Mirjalili at. El.  ...  Those who are not suffering are labelled as healthy people. This is done on the basis of range of bio-medical voice measurements. Those measurements are taken from 31 people of which 23 have PD. .  ... 
doi:10.5281/zenodo.4743516 fatcat:eziwo4k3gbavhanu3t53vbf6zq

Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels [article]

Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel
2021 arXiv   pre-print
applications, such as hierarchical and multi-output labels.  ...  instances.  ...  Jochen Görtler is supported in part by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -Project-ID 251654672 -TRR 161.  ... 
arXiv:2110.12536v1 fatcat:znvs5mbkkfh7lixocijo5ban7y

Learning Preferences for Large Scale Multi-label Problems [chapter]

Ivano Lauriola, Mirko Polato, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli
2018 Lecture Notes in Computer Science  
, as in the case of single-label multi-class and multi-label classification problems.  ...  , as in the case of single-label multi-class and multi-label classification problems.  ...  Broadly speaking, the EC-PLM performs an alternate optimization procedure to learn its parameters.  ... 
doi:10.1007/978-3-030-01418-6_54 fatcat:cikkk4ulyrcr5ad6kx343uiafm

Multi-Label Zero-Shot Learning via Concept Embedding [article]

Ubai Sandouk, Ke Chen
2016 arXiv   pre-print
While most research in ZSL focuses on single-label classification, few studies have been done in multi-label ZSL, where an instance is associated with a set of labels simultaneously, due to the difficulty  ...  In this paper, we propose a novel approach to multi-label ZSL via concept embedding learned from collections of public users' annotations of multimedia.  ...  Therefore, scalable CE learning is an issue that has to be addressed in CE-ML-ZSL.  ... 
arXiv:1606.00282v1 fatcat:iwemt2glm5a6xaifmor6s7rpsq

Integrated instance- and class-based generative modeling for text classification

Antti Puurula, Sung-Hyon Myaeng
2013 Proceedings of the 18th Australasian Document Computing Symposium on - ADCS '13  
TDM is evaluated for classification accuracy on 14 different datasets for multi-label, multi-class and binaryclass text classification tasks and compared to instance-and class-based learning baselines.  ...  Statistical methods for text classification are predominantly based on the paradigm of class-based learning that associates class variables with features, discarding the instances of data after model training  ...  However, in general the use of individual documents increases the data sparsity problem, and basic applications of instance-based learning fail to reach the performance of class-based learning in text  ... 
doi:10.1145/2537734.2537751 dblp:conf/adcs/PuurulaM13 fatcat:v4v54jahf5ehzcqnoffe4gr7am

Offshore Software Maintenance Outsourcing: Predicting Client's Proposal using Supervised Learning

2021 International Journal of Advanced Trends in Computer Science and Engineering  
The dataset is generated through a survey of OSMO vendors working in a developing country.  ...  In software engineering, software maintenance is the process of correction, updating, and improvement of software products after handed over to the customer.  ...  We are thankful to Universiti Malaysia Terengganu, Malaysia for providing state of the art research facilities.  ... 
doi:10.30534/ijatcse/2021/151012021 fatcat:rrsifsvgxffize36hjs2isbbbu

Conditional Graphical Lasso for Multi-label Image Classification

Qiang Li, Maoying Qiao, Wei Bian, Dacheng Tao
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms  ...  By utilizing label correlations, various techniques have been developed to improve classification performance.  ...  Experiments In this section, we evaluate the performance of CGL on the task of multi-label image classification.  ... 
doi:10.1109/cvpr.2016.325 dblp:conf/cvpr/LiQBT16 fatcat:or4exjugtjbzfmsxggtov2qasu

Performance comparison of multi-label learning algorithms on clinical data for chronic diseases

Damien Zufferey, Thomas Hofer, Jean Hennebert, Michael Schumacher, Rolf Ingold, Stefano Bromuri
2015 Computers in Biology and Medicine  
Our goal is to provide a performance comparison of state-of-the-art multi-label learning algorithms for the analysis of multivariate sequential clinical data from medical records of patients affected by  ...  However, the RAkEL algorithm, despite its scalability problems when it is confronted to large dataset, performs well in the scenario which consists of the ranking of the labels according to the dominant  ...  Concerning the ML-kNN, often used as the gold standard in multilabel classification tasks, it obtains a performance very close to decision trees, but it is not very scalable, as the other instance-based  ... 
doi:10.1016/j.compbiomed.2015.07.017 pmid:26275389 fatcat:ceyoalynizeifpds5yiyic3jwu

Supervised Classification Problems – Taxonomy of Dimensions and Notation for Problems Identification

Ireneusz Czarnowski, Piotr Jedrzejowicz
2021 IEEE Access  
The notation consists of 5 fields representing, in a sequence, a structure and properties of decision classes, structural model and properties of attributes, features of the data source, and the performance  ...  The proposed notation is open and could be extended in the case of need new developments within the machine learning theory.  ...  In multi-label learning, the aim is to learn model mapping instances to the powerset of the decision categories set C.  ... 
doi:10.1109/access.2021.3125622 fatcat:monqkucrebccnmfjscs7ytoa44

Resource monitoring and management with OVIS to enable HPC in cloud computing environments

Jim Brandt, Ann Gentile, Jackson Mayo, Philippe Pebay, Diana Roe, David Thompson, Matthew Wong
2009 2009 IEEE International Symposium on Parallel & Distributed Processing  
like an attractive solution for people who currently invest millions to hundreds of millions of dollars annually on High Performance Computing (HPC) platforms in order to support large-scale scientific  ...  scalability and reliability, crucial to HPC applications.  ...  Note, however, that there are other red-colored instances in the figure for which a combination of both metrics led to outliers in the multi-correlative case.  ... 
doi:10.1109/ipdps.2009.5161234 dblp:conf/ipps/BrandtGMPRTW09 fatcat:phrckpu67zaknd4nhuadgwsc5a
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