Coordination number prediction using learning classifier systems: performance and interpretability

Authors:
Jaume Bacardit;Michael Stout;Natalio Krasnogor;Jonathan D. Hirst;Jacek Blazewicz
Affiliations:
University of Nottingham, Nottingham, UK;University of Nottingham, Nottingham, UK;University of Nottingham, Nottingham, UK;University of Nottingham, Nottingham, UK;Poznan University of Technology, Poznan, Poland
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 13
Cited 7

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
C4.5: programs for machine learning

C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning

Machine Learning - Special issue on genetic algorithms
Bagging predictors

Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Discretization: An Enabling Technique

Data Mining and Knowledge Discovery
Prediction of Contact Maps Using Support Vector Machines

BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
The principled design of large-scale recursive neural network architectures--dag-rnns and the protein structure prediction problem

The Journal of Machine Learning Research
Analysis of the initialization stage of a Pittsburgh approach learning classifier system

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Striped sheets and protein contact prediction

Bioinformatics
Classifier fitness based on accuracy

Evolutionary Computation
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Automated alphabet reduction method with evolutionary algorithms for protein structure prediction

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead

Learning Classifier Systems
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System

Learning Classifier Systems
A tale of human-competitiveness in bioinformatics

ACM SIGEVOlution
Evolutionary design of the energy function for protein structure prediction

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
GP challenge: evolving energy function for protein structure prediction

Genetic Programming and Evolvable Machines
Better GP benchmarks: community survey results and proposals

Genetic Programming and Evolvable Machines

Quantified Score

Hi-index	0.00

Visualization

Abstract

The prediction of the coordination number (CN) of an amino acid in a protein structure has recently received renewed attention. In a recent paper, Kinjo et al. proposed a real-valued definition of CN and a criterion to map it onto a finite set of classes, in order to predict it using classification approaches. The literature reports several kinds of input information used for CN prediction. The aim of this paper is to assess the performance of a state-of-the-art learning method, Learning Classifier Systems (LCS) on this CN definition, with various degrees of precision, based on several combinations of input attributes. Moreover, we will compare the LCS performance to other well-known learning techniques. Our experiments are also intended to determinethe minimum set of input information needed to achieve good predictive performance, so as to generate competent yet simple and interpretable classification rules. Thus, the generated predictors (rule sets) are analyzed for their interpretability.