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Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance-partial least squares discriminant analysis when the signal-to-noise ratio and training sample size are sufficient.<\/jats:p>","DOI":"10.3390\/make1040061","type":"journal-article","created":{"date-parts":[[2019,11,5]],"date-time":"2019-11-05T06:47:57Z","timestamp":1572936477000},"page":"1084-1099","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis"],"prefix":"10.3390","volume":"1","author":[{"given":"Luyun","family":"Gan","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, EOW 448, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3514-0073","authenticated-orcid":false,"given":"Brosnan","family":"Yuen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, EOW 448, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1303-0407","authenticated-orcid":false,"given":"Tao","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, EOW 448, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,5]]},"reference":[{"key":"ref_1","first-page":"2683","article-title":"Neural networks and the classification of mineralogical samples using X-ray spectra","volume":"Volume 5","author":"Gallagher","year":"2002","journal-title":"Proceedings of the 2002 9th International Conference on Neural Information Processing (ICONIP\u201902)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1109\/JSEN.2017.2788871","article-title":"Tdlas-based detection of dissolved methane in power transformer oil and field application","volume":"18","author":"Jiang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.trac.2018.11.039","article-title":"Rapid and real-time analysis of volatile compounds released from food using infrared and laser spectroscopy","volume":"110","author":"Dong","year":"2019","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.compag.2007.02.010","article-title":"Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy","volume":"61","author":"Christy","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8587","DOI":"10.1109\/JSEN.2018.2865508","article-title":"Tdlas detection of propane\/butane gas mixture by using reference gas absorption cells and partial least square approach","volume":"18","author":"Wang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1002\/jrs.2702","article-title":"Identification and classification of organic and inorganic components of particulate matter via raman spectroscopy and chemometric approaches","volume":"42","author":"Schumacher","year":"2011","journal-title":"J. Raman Spectrosc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0924-2031(03)00045-6","article-title":"Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules","volume":"32","author":"Goodacre","year":"2003","journal-title":"Vib. Spectrosc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1023\/A:1009982220290","article-title":"An evaluation of statistical approaches to text categorization","volume":"1","author":"Yang","year":"1999","journal-title":"Inf. Retr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1023\/A:1007649029923","article-title":"Boostexter: A boosting-based system for text categorization","volume":"39","author":"Schapire","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/jdwm.2007070101","article-title":"Multi-label classification: An overview","volume":"3","author":"Tsoumakas","year":"2006","journal-title":"Int. J. Data Warehous. Min."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1002\/widm.1139","article-title":"Multi-label learning: A review of the state of the art and ongoing research","volume":"4","author":"Gibaja","year":"2014","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_12","unstructured":"Zhang, Y., and Schneider, J. (July, January 26). Maximum margin output coding. Proceedings of the 29th International Coference on International Conference on Machine Learning (ICML\u201912), Edinburgh, UK."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/TPAMI.2014.2339815","article-title":"Lift: Multi-label learning with label-specific features","volume":"37","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, M.-L., and Zhang, K. (2010, January 25\u201328). Multi-label learning by exploiting label dependency. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201910), Washington, DC, USA.","DOI":"10.1145\/1835804.1835930"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3801","DOI":"10.1109\/TIP.2016.2577382","article-title":"Correlated logistic model with elastic net regularization for multilabel image classification","volume":"25","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Q., Qiao, M., Bian, W., and Tao, D. (2016, January 27\u201330). Conditional graphical lasso for multi-label image classification. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.325"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Godbole, S., and Sarawagi, S. (2004). Discriminative Methods for Multi-Labeled Classification. Advances in Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-540-24775-3_5"},{"key":"ref_18","unstructured":"Katakis, I., Tsoumakas, G., and Vlahavas, I. (2008, January 15\u201319). Multilabel text classification for automated tag suggestion. Proceedings of the ECML PKDD Discovery Challenge, Antwerp, Belgium."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tsoumakas, G., and Vlahavas, I. (2007). Random k-Labelsets: An Ensemble Method for Multilabel Classification. European Conference on Machine Learning, Springer.","DOI":"10.1007\/978-3-540-74958-5_38"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Read, J., Pfahringer, B., Holmes, G., and Frank, E. (2009). Classifier Chains for Multi-Label Classification. Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-642-04174-7_17"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1007\/s10994-017-5659-z","article-title":"Cost-sensitive label embedding for multi-label classification","volume":"106","author":"Huang","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_22","unstructured":"Szyma\u0144ski, P., Kajdanowicz, T., and Chawla, N. (2018). LNEMLC: Label Network Embeddings for Multi-Label Classifiation. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szyma\u0144ski, P., Kajdanowicz, T., and Kersting, K. (2016). How is a data-driven approach better than random choice in label space division for multi-label classification?. Entropy, 18.","DOI":"10.3390\/e18080282"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Clare, A., and King, R.D. (2001). Knowledge Discovery in Multi-Label Phenotype Data. European Conference on Principles of Data Mining and Knowledge Discovery, Springer.","DOI":"10.1007\/3-540-44794-6_4"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/GRC.2005.1547385","article-title":"A k-nearest neighbor based algorithm for multi-label classification","volume":"Volume 2","author":"Zhang","year":"2005","journal-title":"Proceedings of the 2005 IEEE International Conference on Granular Computing"},{"key":"ref_26","unstructured":"Younes, Z., Abdallah, F., and Den\u0153ux, T. (2008, January 25\u201329). Multi-label classification algorithm derived from k-nearest neighbor rule with label dependencies. Proceedings of the 2008 16th European Signal Processing Conference, Lausanne, Switzerland."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Read, J., and Hollm\u00e9n, J. (2015). Multi-label classification using labels as hidden nodes. arXiv.","DOI":"10.1109\/ICDM.2014.38"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","article-title":"Multilabel neural networks with applications to functional genomics and text categorization","volume":"18","author":"Zhang","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nam, J., Kim, J., Menc\u00eda, E.L., Gurevych, I., and F\u00fcrnkranz, J. (2014). Large-Scale Multi-Label Text Classification- Revisiting Neural Networks. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-662-44851-9_28"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_31","unstructured":"Gong, Y., Jia, Y., Leung, T., Toshev, A., and Ioffe, S. (2013). Deep convolutional ranking for multilabel image annotation. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., and Xu, W. (2016, January 27\u201330). Cnn-rnn: A unified framework for multi-label image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.251"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.jqsrt.2013.07.002","article-title":"The HITRAN 2012 Molecular Spectroscopic Database","volume":"130","author":"Rothman","year":"2013","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_34","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","unstructured":"Holland, S.M. (2008). Principal Components Analysis (PCA), Department of Geology, University of Georgia."},{"key":"ref_36","unstructured":"Allred, C.S. (2019, November 01). Partially Correlated Uniformly Distributed Random Numbers. Available online: https:\/\/medium.com\/capital-one-tech\/partially-correlated-uniformly-distributed-random-numbers-5ce82486b68a."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s13748-012-0030-x","article-title":"Binary relevance efficacy for multilabel classification","volume":"1","author":"Luaces","year":"2012","journal-title":"Prog. Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Madden, M.G., and Howley, T. (2009). A Machine Learning Application for Classification of Chemical Spectra. Applications and Innovations in Intelligent Systems XVI, Springer.","DOI":"10.1007\/978-1-84882-215-3_6"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression: A tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_40","first-page":"28","article-title":"Classification of raw milk by infrared spectroscopy (ftir) and chemometric","volume":"1","author":"Elbassbasi","year":"2010","journal-title":"J. Sci. Specul. Res."},{"key":"ref_41","first-page":"59","article-title":"Classification and quality control of lubricating oils by infrared spectroscopy and chemometric","volume":"3","author":"Hirri","year":"2013","journal-title":"Int. J. Adv. Technol. Eng. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1007\/s12161-015-0255-y","article-title":"Ftir spectroscopy and pls-da classification and prediction of four commercial grade virgin olive oils from morocco","volume":"9","author":"Hirri","year":"2016","journal-title":"Food Anal. 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