IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of multiple classifiers for intrusion detection in computer networks
Pattern Recognition Letters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Can machine learning be secure?
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Nightmare at test time: robust learning by feature deletion
ICML '06 Proceedings of the 23rd international conference on Machine learning
Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
The Journal of Machine Learning Research
An enhanced classifier fusion model for classifying biomedical data
International Journal of Computational Vision and Robotics
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The availability of an ever increasing amount of data sources due to recent advances in high throughput biotechnologies opens unprecedented opportunities for genome-wide gene function prediction. Several approaches to integrate heterogeneous sources of biomolecular data have been proposed in literature, but they suffer of drawbacks and limitations that we could in principle overcome by applying multiple classifier systems. In this work we evaluated the performances of three basic ensemble methods to integrate six different sources of high-dimensional biomolecular data. We also studied the performances resulting from the application of a simple greedy classifier selection scheme, and we finally repeated the entire experiment by introducing a feature filtering step. The experimental results show that data fusion realized by means of ensemble-based systems is a valuable research line for gene function prediction.