Prototypes Generation from Multi-label Datasets Based on Granular Computing
暂无分享,去创建一个
Koen Vanhoof | Gonzalo Nápoles | Rafael Bello | Marilyn Bello | K. Vanhoof | Rafael Bello | M. Bello | G. Nápoles
[1] Lotfi A. Zadeh,et al. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..
[2] Grigorios Tsoumakas,et al. Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.
[3] Jerzy W. Grzymala-Busse,et al. Rough Sets , 1995, Commun. ACM.
[4] Donghai Guan,et al. Nearest neighbor editing aided by unlabeled data , 2009, Inf. Sci..
[5] B. John Oommen,et al. A brief taxonomy and ranking of creative prototype reduction schemes , 2003, Pattern Analysis & Applications.
[6] Witold Pedrycz,et al. Granular Computing: At the Junction of Rough Sets and Fuzzy Sets , 2008 .
[7] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[8] Charu C. Aggarwal,et al. Feature Selection for Classification: A Review , 2014, Data Classification: Algorithms and Applications.
[9] Francisco Herrera,et al. A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[10] Witold Pedrycz,et al. Building the fundamentals of granular computing: A principle of justifiable granularity , 2013, Appl. Soft Comput..
[11] Ming Sun,et al. Granular Rough Theory: A representation semantics oriented theory of roughness , 2009, Appl. Soft Comput..
[12] Daniel Vanderpooten,et al. A Generalized Definition of Rough Approximations Based on Similarity , 2000, IEEE Trans. Knowl. Data Eng..
[13] Francisco Charte,et al. R Ultimate Multilabel Dataset Repository , 2016, HAIS.
[14] Andrzej Skowron,et al. Rough sets: Some extensions , 2007, Inf. Sci..
[15] Fernando Fernández,et al. A prototype-based method for classification with time constraints: a case study on automated planning , 2010, Pattern Analysis and Applications.
[16] Francisco Herrera,et al. A memetic algorithm for evolutionary prototype selection: A scaling up approach , 2008, Pattern Recognit..
[17] Rafael Bello,et al. An Approach for Prototype Generation based on Similarity Relations for Problems of Classification , 2015, Computación y Sistemas.
[18] Min-Ling Zhang,et al. A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.
[19] Yiyu Yao,et al. Granular computing using information tables , 2002 .
[20] Tony R. Martinez,et al. Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..
[21] Zhi-Hua Zhou,et al. ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..
[22] Francisco Charte,et al. Multilabel Classification: Problem Analysis, Metrics and Techniques , 2016 .
[23] Francisco Herrera,et al. Data Preprocessing in Data Mining , 2014, Intelligent Systems Reference Library.
[24] Juan Ramón Rico-Juan,et al. Improving kNN multi-label classification in Prototype Selection scenarios using class proposals , 2015, Pattern Recognit..
[25] Loris Nanni,et al. Prototype reduction techniques: A comparison among different approaches , 2011, Expert Syst. Appl..
[26] Vladik Kreinovich,et al. Handbook of Granular Computing , 2008 .
[27] Witold Pedrycz,et al. Granular computing: an introduction , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).
[28] James C. Bezdek,et al. Nearest prototype classifier designs: An experimental study , 2001, Int. J. Intell. Syst..
[29] Sergio Bermejo,et al. A Batch Learning Vector Quantization Algorithm for Nearest Neighbour Classification , 2004, Neural Processing Letters.