{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:20:27Z","timestamp":1762341627005,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Prog Artif Intell"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s13748-021-00237-3","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T14:03:05Z","timestamp":1615298585000},"page":"283-295","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic feature weighting for multi-label classification problems"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1886-4790","authenticated-orcid":false,"given":"Maryam","family":"Dialameh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Hamzeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,9]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: European Conference on Machine Learning, pp. 217\u2013226 (2004)","key":"237_CR1","DOI":"10.1007\/978-3-540-30115-8_22"},{"unstructured":"Kazawa, H., Izumitani, T., Taira, H., Maeda, E.: Maximal margin labeling for multi-topic text categorization. In: Advances in Neural Information Processing Systems, pp. 649\u2013656 (2005)","key":"237_CR2"},{"issue":"3","key":"237_CR3","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1016\/j.eswa.2014.08.036","volume":"42","author":"SM Liu","year":"2015","unstructured":"Liu, S.M., Chen, J.-H.: A multi-label classification based approach for sentiment classification. Expert Syst. Appl. 42(3), 1083\u20131093 (2015)","journal-title":"Expert Syst. Appl."},{"unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: Cnn-rnn: a unified framework for multi-label image classification. In: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, pp. 2285\u20132294 (2016)","key":"237_CR4"},{"key":"237_CR5","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.patrec.2013.11.007","volume":"41","author":"LE Sucar","year":"2014","unstructured":"Sucar, L.E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H., Larra\u00f1aga, P.: Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn. Lett. 41, 14\u201322 (2014)","journal-title":"Pattern Recognit. Lett."},{"key":"237_CR6","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.eswa.2017.09.020","volume":"91","author":"V Kumar","year":"2018","unstructured":"Kumar, V., Pujari, A.K., Padmanabhan, V., Sahu, S.K., Kagita, V.R.: Multi-label classification using hierarchical embedding. Expert Syst. Appl. 91, 263\u2013269 (2018)","journal-title":"Expert Syst. Appl."},{"unstructured":"Bhatia, K., Jain, H., Kar, P., Varma M., Jain, P.: Sparse local embeddings for extreme multi-label classification. In: Advances in Neural Information Processing Systems, pp. 730\u2013738 (2015).","key":"237_CR7"},{"doi-asserted-by":"crossref","unstructured":"Tong, X., Ozturk, P., Gu, M.: Dynamic feature weighting in nearest neighbor classifiers. In: Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, vol. 4, pp. 2406\u20132411 (2004)","key":"237_CR8","DOI":"10.1109\/ICMLC.2004.1382206"},{"key":"237_CR9","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.eswa.2016.12.016","volume":"72","author":"M Dialameh","year":"2017","unstructured":"Dialameh, M., Jahromi, M.Z.: A general feature-weighting function for classification problems. Expert Syst. Appl. 72, 177\u2013188 (2017)","journal-title":"Expert Syst. Appl."},{"unstructured":"Dialameh, M., Jahromi, M.Z.: Dynamic feature weighting for imbalanced data sets. In: Signal Processing and Intelligent Systems Conference (SPIS), 2015, pp. 31\u201336 (2015)","key":"237_CR10"},{"key":"237_CR11","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.entcs.2013.02.010","volume":"292","author":"N Spola\u00f4R","year":"2013","unstructured":"Spola\u00f4R, N., Cherman, E.A., Monard, M.C., Lee, H.D.: A comparison of multi-label feature selection methods using the problem transformation approach. Electron. Notes Theor. Comput. Sci. 292, 135\u2013151 (2013)","journal-title":"Electron. Notes Theor. Comput. Sci."},{"unstructured":"Kong, D., Ding, C., Huang, H., Zhao, H.: Multi-label relief and f-statistic feature selections for image annotation. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2352\u20132359 (2012).","key":"237_CR12"},{"key":"237_CR13","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.ins.2014.09.020","volume":"293","author":"J Lee","year":"2015","unstructured":"Lee, J., Kim, D.-W.: Memetic feature selection algorithm for multi-label classification. Inf. Sci. (Ny) 293, 80\u201396 (2015)","journal-title":"Inf. Sci. (Ny)"},{"issue":"8","key":"237_CR14","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1007\/s13042-017-0647-y","volume":"9","author":"Z Cai","year":"2018","unstructured":"Cai, Z., Zhu, W.: Multi-label feature selection via feature manifold learning and sparsity regularization. Int. J. Mach. Learn. Cybern. 9(8), 1321\u20131334 (2018)","journal-title":"Int. J. Mach. Learn. Cybern."},{"doi-asserted-by":"publisher","unstructured":"Hu, J., Li, Y., Gao, W., Zhang, P.: Robust multi-label feature selection with dual-graph regularization. Knowl-Based Syst. 203:106126 (2020). https:\/\/doi.org\/10.1016\/j.knosys.2020.106126","key":"237_CR15","DOI":"10.1016\/j.knosys.2020.106126"},{"key":"237_CR16","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.patcog.2019.06.003","volume":"95","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Luo, Z., Li, C., Zhou, C., Li, S.: Manifold regularized discriminative feature selection for multi-label learning. Pattern Recogn. 95, 136\u2013150 (2019)","journal-title":"Pattern Recogn."},{"doi-asserted-by":"crossref","unstructured":"Sun, L., Feng, S., Wang, T., Lang, C., Jin, Y.: Partial multi-label learning by low-rank and sparse decomposition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5016\u20135023 (2019)","key":"237_CR17","DOI":"10.1609\/aaai.v33i01.33015016"},{"key":"237_CR18","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/j.patcog.2017.09.036","volume":"74","author":"P Zhu","year":"2018","unstructured":"Zhu, P., Xu, Q., Hu, Q., Zhang, C., Zhao, H.: Multi-label feature selection with missing labels. Pattern Recogn. 74, 488\u2013502 (2018)","journal-title":"Pattern Recogn."},{"key":"237_CR19","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ins.2019.04.021","volume":"492","author":"J Huang","year":"2019","unstructured":"Huang, J., Qin, F., Zheng, X., Cheng, Z., Yuan, Z., Zhang, W., Huang, Q.: Improving multi-label classification with missing labels by learning label-specific features. Inf. Sci. (NY) 492, 124\u2013146 (2019)","journal-title":"Inf. Sci. (NY)"},{"key":"237_CR20","doi-asserted-by":"publisher","first-page":"105052","DOI":"10.1016\/j.knosys.2019.105052","volume":"188","author":"J Gonzalez-Lopez","year":"2020","unstructured":"Gonzalez-Lopez, J., Ventura, S., Cano, A.: Distributed multi-label feature selection using individual mutual information measures. Knowledge-Based Syst. 188, 105052 (2020)","journal-title":"Knowledge-Based Syst."},{"issue":"7","key":"237_CR21","first-page":"2280","volume":"31","author":"J Gonzalez-Lopez","year":"2020","unstructured":"Gonzalez-Lopez, J., Ventura, S., Cano, A.: Distributed selection of continuous features in multilabel classification using mutual information. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2280\u20132293 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"237_CR22","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.neucom.2018.10.047","volume":"329","author":"Z Sun","year":"2019","unstructured":"Sun, Z., Zhang, J., Dai, L., Li, C., Zhou, C., Xin, J., Li, S.: Mutual information based multi-label feature selection via constrained convex optimization. Neurocomputing 329, 447\u2013456 (2019)","journal-title":"Neurocomputing"},{"unstructured":"Zhang, M.-L., Zhou, Z.-H.: A k-nearest neighbor based algorithm for multi-label classification. In: Granular Computing, 2005 IEEE International Conference on, vol. 2, pp. 718\u2013721 (2005)","key":"237_CR23"},{"key":"237_CR24","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.future.2018.04.094","volume":"87","author":"J Gonzalez-Lopez","year":"2018","unstructured":"Gonzalez-Lopez, J., Ventura, S., Cano, A.: Distributed nearest neighbor classification for large-scale multi-label data on spark. Futur. Gener. Comput. Syst. 87, 66\u201382 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"3","key":"237_CR25","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)","journal-title":"Mach. Learn."},{"issue":"2\u20133","key":"237_CR26","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s11265-016-1137-2","volume":"86","author":"Z Yu","year":"2017","unstructured":"Yu, Z., Hao, H., Zhang, W., Dai, H.: A classifier chain algorithm with K-means for multi-label classification on clouds. J. Signal Process. Syst. 86(2\u20133), 337\u2013346 (2017)","journal-title":"J. Signal Process. Syst."},{"key":"237_CR27","doi-asserted-by":"publisher","first-page":"e242","DOI":"10.7717\/peerj-cs.242","volume":"5","author":"H Gweon","year":"2019","unstructured":"Gweon, H., Schonlau, M., Steiner, S.H.: Nearest labelset using double distances for multi-label classification. PeerJ Comput. Sci. 5, e242 (2019)","journal-title":"PeerJ Comput. Sci."},{"issue":"5","key":"237_CR28","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1016\/j.patcog.2014.11.015","volume":"48","author":"J Calvo-Zaragoza","year":"2015","unstructured":"Calvo-Zaragoza, J., Valero-Mas, J.J., Rico-Juan, J.R.: Improving kNN multi-label classification in prototype selection scenarios using class proposals. Pattern Recogn. 48(5), 1608\u20131622 (2015)","journal-title":"Pattern Recogn."},{"unstructured":"Gouk, H., Pfahringer, B., Cree, M.J.: Learning distance metrics for multi-label classification. In: 8th Asian Conference on Machine Learning, vol. 63, pp. 318\u2013333 (2016)","key":"237_CR29"},{"doi-asserted-by":"crossref","unstructured":"Xu, J.: Multi-label weighted k-nearest neighbor classifier with adaptive weight estimation. In: International Conference on Neural Information Processing, pp. 79\u201388 (2011).","key":"237_CR30","DOI":"10.1007\/978-3-642-24958-7_10"},{"key":"237_CR31","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.neucom.2015.02.045","volume":"161","author":"O Reyes","year":"2015","unstructured":"Reyes, O., Morell, C., Ventura, S.: Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context. Neurocomputing 161, 168\u2013182 (2015)","journal-title":"Neurocomputing"},{"issue":"9","key":"237_CR32","doi-asserted-by":"publisher","first-page":"4819","DOI":"10.1007\/s00521-017-3323-y","volume":"31","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Ding, M.: Decision function with probability feature weighting based on Bayesian network for multi-label classification. Neural Comput. Appl. 31(9), 4819\u20134828 (2019)","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"237_CR33","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.patcog.2005.06.001","volume":"39","author":"R Paredes","year":"2006","unstructured":"Paredes, R., Vidal, E.: Learning prototypes and distances: a prototype reduction technique based on nearest neighbor error minimization. Pattern Recogn. 39(2), 180\u2013188 (2006)","journal-title":"Pattern Recognit."},{"issue":"7","key":"237_CR34","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"M-L Zhang","year":"2007","unstructured":"Zhang, M.-L., Zhou, Z.-H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038\u20132048 (2007)","journal-title":"Pattern Recogn."},{"issue":"11","key":"237_CR35","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/2934664","volume":"59","author":"M Zaharia","year":"2016","unstructured":"Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56\u201365 (2016)","journal-title":"Commun. ACM"},{"issue":"1","key":"237_CR36","first-page":"1235","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D.B., Amde, M., Owen, S.: Mllib: machine learning in apache spark. J. Mach. Learn. Res. 17(1), 1235\u20131241 (2016)","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Shi, S., Chu, X., Li, B.: MG-WFBP: efficient data communication for distributed synchronous SGD algorithms. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 172\u2013180 (2019)","key":"237_CR37","DOI":"10.1109\/INFOCOM.2019.8737367"},{"unstructured":"Lian, X., Zhang, W., Zhang, C., Liu, J.: Asynchronous decentralized parallel stochastic gradient descent. In: International Conference on Machine Learning, pp. 3043\u20133052 (2018)","key":"237_CR38"},{"doi-asserted-by":"crossref","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667\u2013685. Springer, Berlin (2009)","key":"237_CR39","DOI":"10.1007\/978-0-387-09823-4_34"},{"issue":"4","key":"237_CR40","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1162\/1063656043138923","volume":"12","author":"C Mattiussi","year":"2004","unstructured":"Mattiussi, C., Waibel, M., Floreano, D.: Measures of diversity for populations and distances between individuals with highly reorganizable genomes. Evol. Comput. 12(4), 495\u2013515 (2004)","journal-title":"Evol. Comput."}],"container-title":["Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-021-00237-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13748-021-00237-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-021-00237-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T18:28:34Z","timestamp":1724610514000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13748-021-00237-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,9]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["237"],"URL":"https:\/\/doi.org\/10.1007\/s13748-021-00237-3","relation":{},"ISSN":["2192-6352","2192-6360"],"issn-type":[{"type":"print","value":"2192-6352"},{"type":"electronic","value":"2192-6360"}],"subject":[],"published":{"date-parts":[[2021,3,9]]},"assertion":[{"value":"3 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}