Extension of Multi-Objective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification to Many-Objective Optimization
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Hisao Ishibuchi | Yusuke Nojima | Naoki Masuyama | Yuichi Omozaki | H. Ishibuchi | Y. Nojima | Naoki Masuyama | Yuichi Omozaki
[1] Hisao Ishibuchi,et al. Multiobjective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification , 2020, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[2] Amina Adadi,et al. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.
[3] Yi Yang,et al. Weakly Supervised Multilabel Clustering and its Applications in Computer Vision , 2016, IEEE Transactions on Cybernetics.
[4] Grigorios Tsoumakas,et al. MULAN: A Java Library for Multi-Label Learning , 2011, J. Mach. Learn. Res..
[5] Hisao Ishibuchi,et al. Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining , 2004, Advanced information processing.
[6] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[7] G MohanaPrabha,et al. Design and development of an efficient hierarchical approach for multi-label protein function prediction , 2017 .
[8] Hisao Ishibuchi,et al. Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning , 2007, Int. J. Approx. Reason..
[9] Piotr Synak,et al. Multi-Label Classification of Emotions in Music , 2006, Intelligent Information Systems.