Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
NetAcet: prediction of N-terminal acetylation sites
Bioinformatics
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Ensemble classifier for protein fold pattern recognition
Bioinformatics
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
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Flavin mono-nucleotide (FMN) closely evolves in many biological processes. In this study, a computational method was proposed to identify FMN binding sites based on amino acid sequences of proteins only. A modified Position Specific Score Matrix was used to characterize the local environmental sequence information, and a visible improvement of performance was obtained. Also, the ensemble SVM was applied to solve the imbalanced data problem. Additionally, an independent dataset was built to evaluate the practical performance of the method, and a satisfactory accuracy of 87.87% was achieved. It demonstrates that the method is effective in predicting FMN-binding sites.