A resilient voting scheme for improving secondary structure prediction

Authors:
Chittaranjan Hota;Filippo Ledda;Giuliano Armano
Affiliations:
Dept. of Computer Science, BITS, Hyderabad, India;Dept. of Electronics & Computer Engineering, University of Cagliari, Italy;Dept. of Electronics & Computer Engineering, University of Cagliari, Italy
Venue:
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Year:
2011

Citing 5
Cited 0

HYPROSP II-A knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence

Bioinformatics
A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems

Pattern Recognition
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence

Journal of Artificial Intelligence Research
Combining the predictions of multiple classifiers: using competitive learning to initialize neural networks

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A voting scheme to improve the secondary structure prediction

AICCSA '10 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel approach called Resilient Voting Scheme (RVS), which combines different predictors (experts) with the goal of improving the overall accuracy. As combining multiple experts involves uncertainty and imprecise information, the proposed approach cancels out the impact of bad performers while computing a single collective prediction. RVS uses a genetic algorithm to assign a reliability to each expert, by using the Q3 measure as fitness function. A resilient voting is then used to improve the accuracy of the final prediction. RVS has been tested with well known datasets and has been compared with other state-of-the-art combination techniques (i.e., averaging and stacking). Experimental results demonstrate the validity of the approach.