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A Structure Based Multiple Instance Learning Approach for Bacterial Ionizing Radiation Resistance Prediction.

, , , and . KES, volume 159 of Procedia Computer Science, page 342-351. Elsevier, (2019)

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Towards a machine learning approach based on incremental concept formation.. Intell. Data Anal., 8 (3): 267-280 (2004)Adaptive Learning of Nominal Concepts for Supervised Classification., and . KES (1), volume 6276 of Lecture Notes in Computer Science, page 121-130. Springer, (2010)Boosting Formal Concepts to Discover Classification Rules., and . IEA/AIE, volume 5579 of Lecture Notes in Computer Science, page 501-510. Springer, (2009)Protein sequences classification by means of feature extraction with substitution matrices., , and . BMC Bioinform., (2010)Vers un critère d'arrêt de Boosting basé sur la diversité des classifieurs., , and . INFORSID, page 111-126. (2011)Efficient Closure Operators for FCA-Based Classification., and . Int. J. Artif. Intell. Mach. Learn., 10 (2): 79-98 (2020)Développement de méthodes de classification basées sur l'analyse de concepts formels sous la plateforme WEKA., , , and . EGC, volume RNTI-E-19 of Revue des Nouvelles Technologies de l'Information, page 621-622. Cépaduès-Éditions, (2010)Comparing graph-based representations of protein for mining purposes., , and . KDD Workshop on Statistical and Relational Learning in Bioinformatics, page 35-38. ACM, (2009)Diversity Analysis on Boosting Nominal Concepts., , and . PAKDD (1), volume 7301 of Lecture Notes in Computer Science, page 306-317. Springer, (2012)Incremental Rule Production: Towards a Uniform Approach for Knowledge Organization., and . IEA/AIE, page 295-304. Gordon and Breach Science Publishers, (1996)