Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
C4.5: programs for machine learning
C4.5: programs for machine learning
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Minimum spanning trees in hierarchical multiclass support vector machines generation
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Human splice site identification with multiclass support vector machines and bagging
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Protein cellular localization with multiclass support vector machines and decision trees
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Improving Protein Localization Prediction Using Amino Acid Group Based Physichemical Encoding
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
Credit card churn forecasting by logistic regression and decision tree
Expert Systems with Applications: An International Journal
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Many cellular functions are carried out in specific compartments of the cell. The prediction of the cellular localization of a protein is thus related to its function identification. This paper uses two Machine Learning techniques, Support Vector Machines (SVMs) and Decision Trees, in the prediction of the localization of proteins from three categories of organisms: gram-positive and gram-negative bacteria and fungi. For all categories considered, the localization task has multiple classes, which correspond to the possible protein locations. Since SVMs are originally designed for the solution of two-class problems, this paper also investigates and compares several strategies to extend this technique to perform multiclass predictions.