A Pattern-Based Approach for Detecting Pneumatic Failures on Temporary Immersion Bioreactors †

Temporary Immersion Bioreactors (TIBs) are used for increasing plant quality and plant multiplication rates. These TIBs are actioned by mean of a pneumatic system. A failure in the pneumatic system could produce severe damages into the TIB. Consequently, the whole biological process would be aborted, increasing the production cost. Therefore, an important task is to detect failures on a temporary immersion bioreactor system. In this paper, we propose to approach this task using a contrast pattern based classifier. We show that our proposal, for detecting pneumatic failures in a TIB, outperforms other approaches reported in the literature. In addition, we introduce a feature representation based on the differences among feature values. Additionally, we collected a new pineapple micropropagation database for detecting four new types of pneumatic failures on TIBs. Finally, we provide an analysis of our experimental results together with experts in both biotechnology and pneumatic devices.

[1]  Inaudis Cejas,et al.  Photosynthesis and carbon metabolism in plantain (Musa AAB) plantlets growing in temporary immersion bioreactors and during ex vitro acclimatization , 2005, In Vitro Cellular & Developmental Biology - Plant.

[2]  Maritza Escalona,et al.  Temporary immersion bioreactors (TIB) provide a versatile, cost-effective and reproducible in vitro analysis of the response of pineapple shoots to salinity and drought , 2017, Acta Physiologiae Plantarum.

[3]  C. Teisson,et al.  A New Concept of Plant In Vitro Cultivation Liquid Medium: Temporary Immersion , 1995 .

[4]  Jesús Ariel Carrasco-Ochoa,et al.  Effect of class imbalance on quality measures for contrast patterns: An experimental study , 2016, Inf. Sci..

[5]  Guozhu Dong,et al.  Discriminating Gene Transfer and Microarray Concordance Analysis , 2013, Contrast Data Mining.

[6]  Charles X. Ling,et al.  Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.

[7]  H. Etienne,et al.  Temporary immersion systems in plant micropropagation , 2002, Plant Cell, Tissue and Organ Culture.

[8]  Kee-Yoeup Paek,et al.  Application of bioreactor systems for large scale production of horticultural and medicinal plants , 2005 .

[9]  Xiuzhen Zhang,et al.  Overview and Analysis of Contrast Pattern Based Classification , 2013, Contrast Data Mining.

[10]  S. Amâncio,et al.  Ex vitro acclimatization of plantain plantlets micropropagated in temporary immersion bioreactor , 2010, Biologia Plantarum.

[11]  James Bailey,et al.  Contrast Data Mining: Concepts, Algorithms, and Applications , 2012 .

[12]  María José del Jesús,et al.  An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects , 2018, WIREs Data Mining Knowl. Discov..

[13]  Raúl Monroy,et al.  Bagging-TPMiner: a classifier ensemble for masquerader detection based on typical objects , 2017, Soft Comput..

[14]  Ronan Bureau,et al.  Emerging Patterns as Structural Alerts for Computational Toxicology , 2013, Contrast Data Mining.

[15]  Tzung-Pei Hong,et al.  Maintaining the discovered sequential patterns for sequence insertion in dynamic databases , 2014, Eng. Appl. Artif. Intell..

[16]  WebbReis Programmable Logic Controllers , 2015 .

[17]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[18]  Ester Bernadó-Mansilla,et al.  Evolutionary rule-based systems for imbalanced data sets , 2008, Soft Comput..

[19]  S. Amâncio,et al.  The physiology of ex vitro pineapple (Ananas comosus L. Merr. var MD-2) as CAM or C3 is regulated by the environmental conditions: proteomic and transcriptomic profiles , 2013, Plant Cell Reports.

[20]  Vivian Kvist Johannsen,et al.  Bioreactor-based advances in plant tissue and cell culture: challenges and prospects , 2018, Critical reviews in biotechnology.

[21]  Haibo He,et al.  Assessment Metrics for Imbalanced Learning , 2013 .

[22]  Guozhu Dong,et al.  Using Emerging Patterns in Outlier and Rare-Class Prediction , 2013, Contrast Data Mining.

[23]  Jinyan Li,et al.  Emerging Pattern Based Rules Characterizing Subtypes of Leukemia , 2013, Contrast Data Mining.

[24]  K. Paek,et al.  Application of bioreactor systems for large scale production of horticultural and medicinal plants , 2005, Plant Cell, Tissue and Organ Culture.

[25]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[26]  Jesús Ariel Carrasco-Ochoa,et al.  PBC4cip: A new contrast pattern-based classifier for class imbalance problems , 2017, Knowl. Based Syst..

[27]  S. García,et al.  An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .

[28]  José Francisco Martínez Trinidad,et al.  A survey of emerging patterns for supervised classification , 2012, Artificial Intelligence Review.

[29]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[30]  Francisco Herrera,et al.  On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed , 2014, Inf. Sci..

[31]  Maritza Escalona,et al.  Sugarcane (Saccharum sp. Hybrid) Propagated in Headspace Renovating Systems Shows Autotrophic Characteristics and Develops Improved Anti-oxidative Response , 2009, Tropical Plant Biology.

[32]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[33]  M. Kantardzic,et al.  The Use of Emerging Patterns in the Analysis of Gene Expression Profiles for the Diagnosis and Understanding of Diseases , 2005 .

[34]  Keun Ho Ryu,et al.  Emerging Pattern Based Prediction of Heart Diseases and Powerline Safety , 2013, Contrast Data Mining.

[35]  Francisco Herrera,et al.  Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[36]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[37]  Maritza Escalona,et al.  Effect of sucrose, light, and carbon dioxide on plantain micropropagation in temporary immersion bioreactors , 2010, In Vitro Cellular & Developmental Biology - Plant.

[38]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[39]  Kotagiri Ramamohanarao,et al.  A Robust Classifier for Imbalanced Datasets , 2014, PAKDD.

[40]  Maritza Escalona,et al.  Pineapple (Ananas comosus L. Merr) micropropagation in temporary immersion systems , 1999, Plant Cell Reports.

[41]  Dr. W. J. Buchanan The Handbook of Data Communications and Networks , 2004, Springer US.

[42]  S. Amâncio,et al.  Comparison of plantain plantlets propagated in temporary immersion bioreactors and gelled medium during in vitro growth and acclimatization , 2014, Biologia Plantarum.

[43]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[44]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[45]  C. Teisson,et al.  In vitro production of potato microtubers in liquid medium using temporary immersion , 1999, Potato Research.

[46]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[47]  Ryszard S. Michalski,et al.  Revealing Conceptual Structure in Data by Inductive Inference , 1982 .

[48]  José Francisco Martínez Trinidad,et al.  Improving graph-based image classification by using emerging patterns as attributes , 2016, Eng. Appl. Artif. Intell..

[49]  Jesús Alcalá-Fdez,et al.  KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..

[50]  Sattar Hashemi,et al.  DFP-SEPSF: A dynamic frequent pattern tree to mine strong emerging patterns in streamwise features , 2015, Eng. Appl. Artif. Intell..

[51]  Luis A. Trejo,et al.  Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data , 2016, Sensors.

[52]  David A. Cieslak,et al.  Hellinger distance decision trees are robust and skew-insensitive , 2011, Data Mining and Knowledge Discovery.

[53]  José Francisco Martínez Trinidad,et al.  Detecting Pneumatic Failures on Temporary Immersion Bioreactors , 2016, MCPR.

[54]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[55]  Krzysztof Walczak,et al.  Emerging Patterns and Classification for Spatial and Image Data , 2013, Contrast Data Mining.