|تعداد مشاهده مقاله||15,580,386|
|تعداد دریافت فایل اصل مقاله||10,888,305|
|Control and Optimization in Applied Mathematics|
|مقاله 3، دوره 6، شماره 2، مهر 2021، صفحه 37-52 اصل مقاله (792.37 K)|
|نوع مقاله: Research Article|
|شناسه دیجیتال (DOI): 10.30473/coam.2021.60849.1177|
|Seyed Hossein Seyed Ebrahimi1؛ Kambiz Majidzadeh* 2؛ Farhad Soleimanian Gharehchopogh2|
|1Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran|
|2Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran|
|Classification is a crucial process in data mining, data science, machine learning, and the applications of natural language processing. Classification methods distinguish the correlation between the data and the output classes. In single-label classification (SLC), each input sample is associated with only one class label. In certain real-world applications, data instances may be assigned to more than one class. The type of classification which is required in such applications is known as multi-label classification (MLC). In MLC, each sample of data is associated with a set of labels. Due to the presence of multiple class labels, the SLC learning process is not applicable to MLC tasks. Many solutions to the multi-label classification problem have been proposed, including BR, FS-DR, and LLSF. But, these methods are not as accurate as they could be. In this paper, a new multi-label classification method is proposed based on graph representation. A feature selection technique and the Q-learning method are employed to increase the accuracy of the proposed algorithm. The proposed multi-label classification algorithm is applied to various standard multi-label datasets. The results are compared with state-of-the-art algorithms based on the well-known performance evaluation metrics. Experimental results demonstrated the effectiveness of the proposed model and its superiority over the other methods.|
|Machine learning؛ Classification؛ Multi-label؛ Meta-label specific features|
 Alizadeh J., Khaloozadeh H. (2019). “Enlarging the region of attraction for nonlinear systems through the sum-of-squares programming”, Control and Optimization in Applied Mathematics, 4(2), 19-37.
 Al-Makhadmeh Z., Tolba A. (2019). “Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach”, Measurement, 147, 106815.
 Blondel V., et al. (2008). “Fast unfolding of communities in large networks”, Journal of Statistical Mechanics: Theory and Experiment, 10008, 1-12.
 Boutell M.R., Luo J., Shen X., Brown C.M. (2004). “Learning multi-label scene classification”, Pattern Recognition, 1757-1771.
 Dong H., et al. (2020). “A many-objective feature selection for multi-label classification”, Knowledge-Based Systems, 208, 106456.
 Hashemi A., Dowlatshahi M.B., Nezamabadi-pour H. (2020). “MGFS: A multi-label graph-based feature selection algorithm via PageRank centrality”, Expert Systems with Applications, 142, 113024.
 Hu J., et al. (2020). “Robust multi-label feature selection with dual-graph regularization”, Knowledge-Based Systems, 203, 106126.
 Jayaraman V., Sultana H.P. (2019). “Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification”, Journal of Ambient Intelligence and Humanized Computing.
 Ji Z., et al. (2020). “Deep ranking for image zero-shot multi-label classification”, IEEE Transactions on Image Processing, 29, 6549-6560.
 Khammassi C., Krichen S. (2017). “A GA-LR wrapper approach for feature selection in network intrusion detection”, Computers & Security, 70, 255-277.
 Khandagale S., Xiao H., Babbar R. (2020). “Bonsai: diverse and shallow trees for extreme multi-label classification”, Machine Learning, 109(11), 2099-2119.
 Mafarja M., Mirjalili S. (2018). “Whale optimization approaches for wrapper feature selection”, Applied Soft Computing, 62, 441-453.
 Makki I., Alhalabi W., Adham R.S. (2019). “Using emotion analysis to define human factors of virtual reality wearables”, Procedia Computer Science, 163, 154-164.
 Mansourinasab S., Sojoodi M., Moghadasi S.R. (2019). “Model predictive control for a 3D pendulum on SO (3) manifold using convex optimization”, Control and Optimization in Applied Mathematics, 4(2), 69-80.
 MonirulKabir Md., Shahjahan Md., Murase K. (2011). “A new local search based hybrid genetic algorithm for feature selection”, Neurocomputing, 74(17), 2914-2928.
 Morais-Rodrigues F., et al. (2020). “Analysis of the microarray gene expression for breast cancer progression after the application modified logistic regression”, Gene, 726, 144168.
 Naem A.A., Ghali N.I., Saleh A.A. (2018). “Antlion optimization and boosting classifier for spam email detection”, Future Computing and Informatics Journal, 3(2), 436-442.
 Nasarian E., et al. (2020). “Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach”, Pattern Recognition Letters, 133, 33-40.
 Prajapati P., Thakkar A. (2021). “Performance improvement of extreme multi-label classification using K-way tree construction with parallel clustering algorithm”, Journal of King Saud University - Computer and Information Sciences.
 Rejer I., Twardochleb M. (2018). “Gamers’ involvement detection from EEG data with cGAAM – A method for feature selection for clustering”, Expert Systems with Applications, 101, 196-204.
 Rostami M., Berahmand K., Forouzandeh S. (2020). “A novel method of constrained feature selection by the measurement of pairwise constraints uncertainty”, Journal of Big Data, 7(1), 83.
 Salman M.S., et al. (2019). “Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression”, NeuroImage: Clinical, 22, 101747.
 Song D., et al. (2021). “Multi-label legal document classification: A deep learning-based approach with label-attention and domain-specific pre-training”, Information Systems, 101718.
 Song Q., et al. (2017). “Using deep learning for classification of lung nodules on computed tomography images”, Journal of healthcare engineering, 2017.
 Sun L., Kudo M., Kimura, K. (2016, December). “Multi-label classification with meta label-specific features”, In 2016 23rd International Conference on Pattern Recognition (ICPR) (1612-1617). IEEE.
 Thabtah F.A., Cowling P., Peng Y., Rastogi R., Morik K., Bramer M., Wu X. (2004). “MMAC: A new multi-class, multi-label associative classification approach”, Proc. Proceedings of Fourth IEEE International Conference on Data Mining, ICDM 2004, 217-224.
 Theodoridis S., Koutroumbas K. (2008). “Pattern recognition”, Academic Press, Oxford.
 Wang W., et al. (2020). “MLCDForest: Multi-label classification with deep forest in disease prediction for long non-coding RNAs”, Briefings in Bioinformatics.
 Wu T., et al. (2020). “Distribution-balanced loss for multi-label classification in long-tailed datasets”, in European Conference on Computer Vision, Springer.
 Xia Y., Chen K., Yang Y. (2021). “Multi-label classification with weighted classifier selection and stacked ensemble”, Information Sciences, 557, 421-442.
 Xu J. (2011). “An extended one-versus-rest support vector machine for multi-label classification”, Neurocomputing, 3114-3124.
 Yang M., et al. (2019). “Investigating the transferring capability of capsule networks for text classification”, Neural Networks, 118, 247-261.
 Yap X.H., Raymer M. (2021). “Multi-label classification and label dependence in in silico toxicity prediction”, Toxicology in Vitro, 105157.
 You R., et al. (2020). “Cross-modality attention with semantic graph embedding for multi-label classification”, in Proceedings of the AAAI Conference on Artificial Intelligence.
 Yun D., Ryu J., Lim J. (2021). “Dual aggregated feature pyramid network for multi-label classification”, Pattern Recognition Letters, 144, 75-81.
 Zheng X., et al. (2019). “Gene selection for microarray data classification via adaptive hypergraph embedded dictionary learning”, Gene, 706, 188-200.
 Zhang Y., et al. (2020). “Large-scale multi-label classification using unknown streaming images”, Pattern Recognition, 99, 107100.
 Zhong Y., Du B., Xu C. (2021). “Learning to reweight examples in multi-label classification”, Neural Networks.
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