Making large-scale support vector machine learning practical
Advances in kernel methods
On Multi-class Problems and Discretization in Inductive Logic Programming
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Margin-based first-order rule learning
Machine Learning
Bioinformatics
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Support vector inductive logic programming
DS'05 Proceedings of the 8th international conference on Discovery Science
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Inductive Logic Programming (ILP) systems have been successfully applied to solve complex biological problem by viewing them as binary classification tasks. It remains an open question how an accurate solution to a multi-class problem can be obtained by using a logic based learning method. In this paper we present a novel logic based approach to solve complex and challenging multi-class classification problems in bioinformatics by focusing on a particular task, namely protein fold recognition. Our technique is based on the use of large margin kernel-based methods in conjunction with first order rules induced by an ILP system. The proposed approach learns a multi-class classifier by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. The method is applied to assigning protein domains to folds. Experimental evaluation of the method demonstrates the efficacy of the proposed approach to solving complex multi-class classification problems in bioinformatics.