An approach for classification of highly imbalanced data using weighting and undersampling
暂无分享,去创建一个
P. N. Suganthan | Gary B. Fogel | Ashish Anand | Ganesan Pugalenthi | G. Fogel | P. Suganthan | G. Pugalenthi | A. Anand
[1] Janet M. Thornton,et al. The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data , 2004, Nucleic Acids Res..
[2] Dunja Mladenic,et al. Feature Selection for Unbalanced Class Distribution and Naive Bayes , 1999, ICML.
[3] Nello Cristianini,et al. Controlling the Sensitivity of Support Vector Machines , 1999 .
[4] X.-D. Sun,et al. Prediction of protein structural classes using support vector machines , 2006, Amino Acids.
[5] Vasile Palade,et al. A New Performance Measure for Class Imbalance Learning. Application to Bioinformatics Problems , 2009, 2009 International Conference on Machine Learning and Applications.
[6] Jacek M. Zurada,et al. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance , 2008, Neural Networks.
[7] George Forman,et al. An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..
[8] Jianping Zhang,et al. Learning rules from highly unbalanced data sets , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[9] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[10] De-Shuang Huang,et al. Predicting protein–protein interactions from sequence using correlation coefficient and high-quality interaction dataset , 2010, Amino Acids.
[11] Corinna Cortes,et al. Prediction of Generalization Ability in Learning Machines , 1994 .
[12] Y. Wang,et al. PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles , 2008, Amino Acids.
[13] Edward Y. Chang,et al. Class-Boundary Alignment for Imbalanced Dataset Learning , 2003 .
[14] K. Nishikawa,et al. Radial locations of amino acid residues in a globular protein: correlation with the sequence. , 1986, Journal of biochemistry.
[15] Neil Davey,et al. Using Real-Valued Meta Classifiers to Integrate and Contextualize Binding Site Predictions , 2007, ICANNGA.
[16] Charlotte M. Deane,et al. JOY: protein sequence-structure representation and analysis , 1998, Bioinform..
[17] Nitesh V. Chawla,et al. Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.
[18] Yanqing Zhang,et al. SVMs Modeling for Highly Imbalanced Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[19] K. Chou,et al. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale , 2007, Amino Acids.
[20] Liam J. McGuffin,et al. The PSIPRED protein structure prediction server , 2000, Bioinform..
[21] Vasile Palade,et al. microPred: effective classification of pre-miRNAs for human miRNA gene prediction , 2009, Bioinform..
[22] Minoru Kanehisa,et al. AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..
[23] Xue-wen Chen,et al. Sequence-based prediction of protein interaction sites with an integrative method , 2009, Bioinform..
[24] Ana Paula Sales,et al. Improving peptide-MHC class I binding prediction for unbalanced datasets , 2008, BMC Bioinformatics.
[25] Stephen Kwek,et al. Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.
[26] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[27] Xiao Sun,et al. Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature , 2008, Bioinform..
[28] Zhi-Hua Zhou,et al. Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[29] K.-C. Chou,et al. Using string kernel to predict signal peptide cleavage site based on subsite coupling model , 2005, Amino Acids.
[30] P. Suganthan,et al. Identification of catalytic residues from protein structure using support vector machine with sequence and structural features. , 2008, Biochemical and biophysical research communications.
[31] Adam Godzik,et al. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences , 2006, Bioinform..
[32] Stan Matwin,et al. Learning When Negative Examples Abound , 1997, ECML.
[33] Louise C. Showe,et al. Bioinformatics Original Paper Combining Multi-species Genomic Data for Microrna Identification Using a Naı¨ve Bayes Classifier , 2022 .
[34] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[35] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[36] Constantin F. Aliferis,et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2004, Bioinform..
[37] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[38] G. Raghava,et al. Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile , 2010, Amino Acids.
[39] Zheng Rong Yang,et al. Biological applications of support vector machines , 2004, Briefings Bioinform..