Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptive Selection of Image Classifiers
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
Selective fusion of heterogeneous classifiers
Intelligent Data Analysis
Issues in stacked generalization
Journal of Artificial Intelligence Research
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On diversity and accuracy of homogeneous and heterogeneous ensembles
International Journal of Hybrid Intelligent Systems
Prediction of Enzyme Class by Using Reactive Motifs Generated from Binding and Catalytic Sites
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Semi-automatic dynamic auxiliary-tag-aided image annotation
Pattern Recognition
Concept lattice-based mutation control for reactive motifs discovery
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Sparse representation: extract adaptive neighborhood for multilabel classification
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Dual layer voting method for efficient multi-label classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Feature selection for multi-label classification problems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
On the stratification of multi-label data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Evolving multi-label classification rules with gene expression programming: a preliminary study
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A model for multi-label classification and ranking of learning objects
Expert Systems with Applications: An International Journal
Feature selection for multi-label classification using multivariate mutual information
Pattern Recognition Letters
A grammar-guided genetic programming algorithm for multi-label classification
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
A system for classifying multi-label text into EuroVoc
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
Optimizing biodiversity prediction from abiotic parameters
Environmental Modelling & Software
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Nowadays, the number of protein sequences being stored in central protein databases from labs all over the world is constantly increasing. From these proteins only a fraction has been experimentally analyzed in order to detect their structure and hence their function in the corresponding organism. The reason is that experimental determination of structure is labor-intensive and quite time-consuming. Therefore there is the need for automated tools that can classify new proteins to structural families. This paper presents a comparative evaluation of several algorithms that learn such classification models from data concerning patterns of proteins with known structure. In addition, several approaches that combine multiple learning algorithms to increase the accuracy of predictions are evaluated. The results of the experiments provide insights that can help biologists and computer scientists design high-performance protein classification systems of high quality.