Fuzzy information decomposition incorporated and weighted Relief-F feature selection: When imbalanced data meet incompletion

[1]  Guisong Yang,et al.  Improved CBSO: A distributed fuzzy-based adaptive synthetic oversampling algorithm for imbalanced judicial data , 2021, Inf. Sci..

[2]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[3]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[4]  Guisong Yang,et al.  Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach , 2020, IEEE Transactions on Industrial Informatics.

[5]  Yu Zhang,et al.  New stability theorem for uncertain pantograph differential equations , 2021, J. Intell. Fuzzy Syst..

[6]  Witold Pedrycz,et al.  A Novel Framework for Imputation of Missing Values in Databases , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[7]  Zizhong Chen,et al.  RSMOTE: A self-adaptive robust SMOTE for imbalanced problems with label noise , 2021, Inf. Sci..

[8]  Zhifu Jia,et al.  Complex uncertain differential equations with application to time integral , 2021, J. Intell. Fuzzy Syst..

[9]  Shyam Visweswaran,et al.  Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease , 2012, BioData Mining.

[10]  Yinhai Wang,et al.  Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory , 2020, J. Intell. Transp. Syst..

[11]  Hadi A. Khorshidi,et al.  Missing data imputation using decision trees and fuzzy clustering with iterative learning , 2019, Knowledge and Information Systems.

[12]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[13]  Kazuyuki Murase,et al.  A Novel Synthetic Minority Oversampling Technique for Imbalanced Data Set Learning , 2011, ICONIP.

[14]  Xiaofeng Zhu,et al.  Efficient Utilization of Missing Data in Cost-Sensitive Learning , 2019, IEEE Transactions on Knowledge and Data Engineering.

[15]  Mahdi Hashemzadeh,et al.  RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem , 2021, Inf. Sci..

[16]  Tong Zhang,et al.  An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data , 2020, Sensors.

[17]  Rui Liu,et al.  Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification , 2019, Inf. Sci..

[18]  Yijun Sun,et al.  Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Kewei Cheng,et al.  Feature Selection , 2016, ACM Comput. Surv..

[20]  Fernando Bação,et al.  Oversampling for Imbalanced Learning Based on K-Means and SMOTE , 2017, Inf. Sci..

[21]  Chih-Fong Tsai,et al.  Clustering-based undersampling in class-imbalanced data , 2017, Inf. Sci..

[22]  Jianmin Wang,et al.  Enriching Data Imputation under Similarity Rule Constraints , 2020, IEEE Transactions on Knowledge and Data Engineering.

[23]  Bartosz Krawczyk,et al.  Radial-Based Oversampling for Multiclass Imbalanced Data Classification , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[25]  Chee Khiang Pang,et al.  Classification of Imbalanced Data by Oversampling in Kernel Space of Support Vector Machines , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Juan Miguel Morales,et al.  A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification , 2020, Eur. J. Oper. Res..

[27]  Jun Zhang,et al.  Fuzzy-Based Information Decomposition for Incomplete and Imbalanced Data Learning , 2017, IEEE Transactions on Fuzzy Systems.

[28]  Chih-Fong Tsai,et al.  A class center based approach for missing value imputation , 2018, Knowl. Based Syst..

[29]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[30]  Haibo Jiang,et al.  A novel three-way group investment decision model under intuitionistic fuzzy multi-attribute group decision-making environment , 2021, Inf. Sci..

[31]  MengChu Zhou,et al.  An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems , 2014, IEEE Transactions on Industrial Informatics.

[32]  Yiqiang Chen,et al.  Weighted extreme learning machine for imbalance learning , 2013, Neurocomputing.

[33]  Bao Qing Hu,et al.  A decision-theoretic fuzzy rough set in hesitant fuzzy information systems and its application in multi-attribute decision-making , 2021, Inf. Sci..

[34]  Feng Zhou,et al.  Nonlinear compensation algorithm for multidimensional temporal data: A missing value imputation for the power grid applications , 2021, Knowl. Based Syst..

[35]  Ke Lu,et al.  Missing data imputation by K nearest neighbours based on grey relational structure and mutual information , 2015, Applied Intelligence.

[36]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[37]  Pawel Karczmarek,et al.  Data Imputation in Related Time Series Using Fuzzy Set-Based Techniques , 2020, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[38]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[39]  Xiaojie Yuan,et al.  Missing value imputation in multivariate time series with end-to-end generative adversarial networks , 2021, Inf. Sci..

[40]  Seyed Mohammad Mirjalili,et al.  Whale optimization approaches for wrapper feature selection , 2018, Appl. Soft Comput..