{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T05:23:50Z","timestamp":1725686630931},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T00:00:00Z","timestamp":1522627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple Graphs, instead of Instances to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the Multi-Graph Multi-Label Learning into the Multi-Instance Multi-Label Learning in order to solve it by MIML-ELM (Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine). The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency.<\/jats:p>","DOI":"10.3390\/e20040245","type":"journal-article","created":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T16:32:20Z","timestamp":1522686740000},"page":"245","source":"Crossref","is-referenced-by-count":7,"title":["Multi-Graph Multi-Label Learning Based on Entropy"],"prefix":"10.3390","volume":"20","author":[{"given":"Zixuan","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Yuhai","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s11045-016-0408-1","article-title":"Encrypted image classification based on multilayer extreme learning machine","volume":"28","author":"Wang","year":"2017","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7875","DOI":"10.1007\/s11042-015-2702-6","article-title":"A New multi-instance multi-label learning approach for image and text classification","volume":"75","author":"Yan","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3385","DOI":"10.1109\/TIP.2016.2642781","article-title":"Learning Multi-Instance Deep Discriminative Patterns for Image Classification","volume":"26","author":"Tang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","first-page":"327","article-title":"Feature Selection Techniques for Breast Cancer Image Classification with Support Vector Machine","volume":"2221","author":"Chaiyakhan","year":"2016","journal-title":"Lect. Notes Eng. Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.ijleo.2015.10.096","article-title":"Texture classification via extended local graph structure","volume":"127","author":"Bashier","year":"2016","journal-title":"Optik Int. J. Light Electron Opt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.compeleceng.2016.03.003","article-title":"Image segmentation incorporating double-mask via graph cuts","volume":"54","author":"Wang","year":"2016","journal-title":"Comput. Electr. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TCYB.2016.2527239","article-title":"Positive and Unlabeled Multi-Graph Learning","volume":"47","author":"Wu","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10115-015-0872-1","article-title":"Multi-graph-view subgraph mining for graph classification","volume":"48","author":"Wu","year":"2016","journal-title":"Knowl. Inf. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2382","DOI":"10.1109\/TKDE.2013.2297923","article-title":"Bag Constrained Structure Pattern Mining for Multi-Graph Classification","volume":"26","author":"Wu","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhu, X., Zhang, C., and Cai, Z. (2013, January 7\u201310). Multi-instance Multi-graph Dual Embedding Learning. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA.","DOI":"10.1109\/ICDM.2013.121"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1109\/TPAMI.2015.2491929","article-title":"HCP: A Flexible CNN Framework for Multi-Label Image Classification","volume":"38","author":"Wei","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.3233\/JIFS-169264","article-title":"Improvised Apriori with frequent subgraph tree for extracting frequent subgraphs","volume":"32","author":"Nair","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.patcog.2015.08.006","article-title":"Fast depth-based subgraph kernels for unattributed graphs","volume":"50","author":"Bai","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_14","unstructured":"Ketkar, N., Holder, L., and Cook, D. (April, January 30). Empirical Comparison of Graph Classification Algorithms. Proceedings of the IEEE Symposium Computational Intelligence and Data Mining (CIDM), Nashville, TN, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Inokuchi, A., Washio, T., and Motoda, H. (2000, January 13\u201316). An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. Proceedings of the Fourth European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), Lyon, France.","DOI":"10.1007\/3-540-45372-5_2"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nijssen, S., and Kok, J. (2004, January 22\u201325). A Quickstart in Frequent Structure Mining can Make a Difference. Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, WA, USA.","DOI":"10.1145\/1014052.1014134"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10994-008-5089-z","article-title":"gBoost: A Mathematical Programming Approach to Graph Classification and Regression","volume":"75","author":"Saigo","year":"2009","journal-title":"Mach. Learn."},{"key":"ref_18","unstructured":"Yan, X., and Han, J. (2002, January 9\u201312). gSpan: Graph-Based Substructure Pattern Mining. Proceedings of the IEEE International Conference on Data Mining (ICDM), Maebashi, Japan."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Kong, X., and Yu, P.S. (2011, January 11\u201314). Positive and Unlabeled Learning for Graph Classification. Proceedings of the IEEE 11th International Conference on Data Mining (ICDM), Vancouver, BC, Canada.","DOI":"10.1109\/ICDM.2011.119"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kong, X., and Yu, P. (2010, January 13\u201317). Multi-Label Feature Selection for Graph Classification. Proceedings of the 10th International Conference on Data Mining (ICDM), Sydney, Australia.","DOI":"10.1109\/ICDM.2010.58"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1109\/TCBB.2014.2323058","article-title":"Genome-wide protein function prediction through multi-instance multi-label learning","volume":"11","author":"Wu","year":"2014","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, Z., and Zhang, M. (2007). Multi-instance multi-label learning with application to scene classification. Advances in Neural Information Processing Systems, The MIT Press.","DOI":"10.7551\/mitpress\/7503.003.0206"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TCBB.2011.73","article-title":"Drosophila gene expression pattern annotation through multi-instance multi-label learning","volume":"9","author":"Li","year":"2012","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yin, Y., Zhao, Y., Li, C., and Zhang, B. (2016). Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine. Appl. Sci., 6.","DOI":"10.3390\/app6060160"},{"key":"ref_25","first-page":"93","article-title":"Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions","volume":"8835","author":"Martino","year":"2014","journal-title":"Neural Inf. Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Seeland, M., and Kramer, A.K.S. (2012, January 12\u201316). A Structural Cluster Kernel for Learning on Graphs. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Beijing, China.","DOI":"10.1145\/2339530.2339614"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/TSMCB.2009.2019264","article-title":"Graph Classification by Means of Lipschitz Embedding","volume":"39","author":"Riesen","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s10270-014-0450-0","article-title":"Feature-based classification of bidirectional transformation approaches","volume":"15","author":"Hidaka","year":"2016","journal-title":"Softw. Syst. Model."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/TKDE.2005.127","article-title":"Frequent Substructure-Based Approaches for Classifying Chemical Compounds","volume":"17","author":"Deshpande","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/asi.23699","article-title":"Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology","volume":"68","author":"Maruping","year":"2017","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1016\/j.physleta.2017.06.028","article-title":"Topological Hausdorff dimension and geodesic metric of critical percolation cluster in two dimensions","volume":"381","author":"Balankin","year":"2017","journal-title":"Phys. Lett. A"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Winn, J., Criminisi, A., and Minka, T. (2005, January 17\u201321). Object categorization by learned universal visual dictionary. Proceedings of the Tenth IEEE International Conference on Computer Vision, Beijing, China.","DOI":"10.1109\/ICCV.2005.171"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1016\/j.artint.2011.10.002","article-title":"Multi-instance multilabel learning","volume":"176","author":"Zhou","year":"2012","journal-title":"Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Duygulu, P., Barnard, K., Freitas, J., and Forsyth, D. (2002, January 28\u201331). Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark.","DOI":"10.1007\/3-540-47979-1_7"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nowozin, S., Tsuda, K., Uno, T., Kudo, T., and Bakir, G. (2007, January 17\u201322). Weighted Substructure Mining for Image Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383171"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/4\/245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T08:14:06Z","timestamp":1718007246000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/4\/245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,2]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["e20040245"],"URL":"https:\/\/doi.org\/10.3390\/e20040245","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,2]]}}}