Name
Title | ||
|---|---|---|
Performance prediction for RNA design using parametric and non-parametric regression models |
Abstract | ||
|---|---|---|
Empirical algorithm study involves tuning various parameter settings in order to achieve an optimal performance. It is also experimentally known that algorithm performance varies across problem instances. In stochastic local search (metaheuristics) paradigm, search efficiency is correlated to the empirical hardness of the underlying combinatorial optimization problem itself. Therefore, investigating these correlations are of crucial importance towards the design of robust algorithmic solutions. To achieve this goal, an accurate prediction of algorithm performance is a prerequisite, since it allows an automatic tuning of parameter settings on a per-problem base. In this work, we investigate using parametric & non-parametric regression models for algorithm performance prediction for the RNA Secondary Structure Design problem (SSD). Empirical results show our non-parametric methods achieve a higher prediction accuracy on biologically existing data, where biological data exhibits a higher degree of local similarity among individual instances. We also found that using a non-parametric regression tree model (CART) provides insight into studying the empirical hardness of solving the SSD problem. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CIBCB.2009.4925702 | Nashville, TN |
Keywords | Field | DocType |
empirical result,empirical hardness,algorithm performance prediction,empirical algorithm study,underlying combinatorial optimization problem,ssd problem,problem instance,secondary structure design problem,algorithm performance,rna design,non-parametric regression model,optimal performance,kernel,regression analysis,algorithm design and analysis,non parametric regression,rna secondary structure,rna,biological data,prediction algorithms,molecular biophysics | Decision tree,Algorithm design,Regression analysis,Computer science,Nonparametric regression,Parametric statistics,Artificial intelligence,Bioinformatics,Local search (optimization),Performance prediction,Machine learning,Metaheuristic | Conference |
ISBN | Citations | PageRank |
978-1-4244-2756-7 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Denny C. Dai | 1 | 6 | 1.18 |
| Kay C. Wiese | 2 | 164 | 19.10 |