Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multi gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance - partial least squares discriminant analysis when signal-to-noise ratio and training sample size are sufficient.

[1]  Sebastián Ventura,et al.  Multi‐label learning: a review of the state of the art and ongoing research , 2014, WIREs Data Mining Knowl. Discov..

[2]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[3]  Jesse Read,et al.  Multi-label Classification using Labels as Hidden Nodes , 2015, ArXiv.

[4]  Yangqing Jia,et al.  Deep Convolutional Ranking for Multilabel Image Annotation , 2013, ICLR.

[5]  CLASSIFICATION AND QUALITY CONTROL OF LUBRICATING OILS BY INFRARED SPECTROSCOPY AND CHEMOMETRIC , 2013 .

[6]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[7]  J. Popp,et al.  Identification and classification of organic and inorganic components of particulate matter via Raman spectroscopy and chemometric approaches , 2011 .

[8]  Johannes Fürnkranz,et al.  Large-Scale Multi-label Text Classification - Revisiting Neural Networks , 2013, ECML/PKDD.

[9]  Nitesh V. Chawla,et al.  LNEMLC: Label Network Embeddings for Multi-Label Classifiation , 2018, ArXiv.

[10]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[11]  D. Dong,et al.  Rapid and real-time analysis of volatile compounds released from food using infrared and laser spectroscopy , 2019, TrAC Trends in Analytical Chemistry.

[12]  C. D. Christy,et al.  Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy , 2008 .

[13]  Hsuan-Tien Lin,et al.  Cost-sensitive label embedding for multi-label classification , 2017, Machine Learning.

[14]  Thierry Denoeux,et al.  Multi-label classification algorithm derived from K-nearest neighbor rule with label dependencies , 2008, 2008 16th European Signal Processing Conference.

[15]  Kun Zhang,et al.  Multi-label learning by exploiting label dependency , 2010, KDD.

[16]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[17]  Michael G. Madden,et al.  A Machine Learning Application for Classification of Chemical Spectra , 2008, SGAI Conf..

[18]  A. Maćkiewicz,et al.  Principal Components Analysis (PCA) , 1993 .

[19]  Royston Goodacre,et al.  Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules , 2003 .

[20]  R. Tauler,et al.  FTIR Spectroscopy and PLS-DA Classification and Prediction of Four Commercial Grade Virgin Olive Oils from Morocco , 2016, Food Analytical Methods.

[21]  K. Grattan,et al.  TDLAS Detection of Propane/Butane Gas Mixture by Using Reference Gas Absorption Cells and Partial Least Square Approach , 2018, IEEE Sensors Journal.

[22]  Jane You,et al.  Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification , 2016, IEEE Transactions on Image Processing.

[23]  Gang Li,et al.  The HITRAN 2008 molecular spectroscopic database , 2005 .

[24]  Marcus Gallagher,et al.  Neural networks and the classification of mineralogical samples using x-ray spectra , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[25]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[26]  Sunita Sarawagi,et al.  Discriminative Methods for Multi-labeled Classification , 2004, PAKDD.

[27]  Kristian Kersting,et al.  How is a data-driven approach better than random choice in label space division for multi-label classification? , 2016, Entropy.

[28]  Quevedo Amaya,et al.  Caracterización fisiológica y bioquímica de cuatro genotipos de algodón (Gossypium hirsutum L.) en condiciones de déficit hídrico , 2020 .

[29]  Grigorios Tsoumakas,et al.  Multilabel Text Classification for Automated Tag Suggestion , 2008 .

[30]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[31]  Grigorios Tsoumakas,et al.  Random k -Labelsets: An Ensemble Method for Multilabel Classification , 2007, ECML.

[32]  Geoff Holmes,et al.  Classifier chains for multi-label classification , 2009, Machine Learning.

[33]  Carranza Díaz,et al.  Espectroscopía de reflectancia difusa – NIR para la determinación del contenido de agua en el suelo , 2020 .

[34]  Jun Jiang,et al.  TDLAS-Based Detection of Dissolved Methane in Power Transformer Oil and Field Application , 2018, IEEE Sensors Journal.

[35]  Zhi-Hua Zhou,et al.  A k-nearest neighbor based algorithm for multi-label classification , 2005, 2005 IEEE International Conference on Granular Computing.

[36]  Amanda Clare,et al.  Knowledge Discovery in Multi-label Phenotype Data , 2001, PKDD.

[37]  Lei Wu,et al.  Lift: Multi-Label Learning with Label-Specific Features , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  E. R. Polovtseva,et al.  The HITRAN2012 molecular spectroscopic database , 2013 .

[39]  Juan José del Coz,et al.  Binary relevance efficacy for multilabel classification , 2012, Progress in Artificial Intelligence.

[40]  Wei Xu,et al.  CNN-RNN: A Unified Framework for Multi-label Image Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Qiang Li,et al.  Conditional Graphical Lasso for Multi-label Image Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Yiming Yang,et al.  An Evaluation of Statistical Approaches to Text Categorization , 1999, Information Retrieval.

[43]  Jeff G. Schneider,et al.  Maximum Margin Output Coding , 2012, ICML.