A Pattern-Based Approach for Detecting Pneumatic Failures on Temporary Immersion Bioreactors †
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Jesús Ariel Carrasco-Ochoa | Raúl Monroy | Milton García-Borroto | Octavio Loyola-González | Miguel Angel Medina-Pérez | Dayton Hernández-Tamayo | R. Monroy | O. Loyola-González | Dayton Hernández-Tamayo | Milton García-Borroto | J. Carrasco-Ochoa | M. A. Medina-Pérez
[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.