{"title":"基于参数和非参数回归模型的RNA设计性能预测","authors":"D. C. Dai, K. Wiese","doi":"10.1109/CIBCB.2009.4925702","DOIUrl":null,"url":null,"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 perproblem 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.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"159 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Performance prediction for RNA design using parametric and non-parametric regression models\",\"authors\":\"D. C. Dai, K. Wiese\",\"doi\":\"10.1109/CIBCB.2009.4925702\",\"DOIUrl\":null,\"url\":null,\"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 perproblem 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.\",\"PeriodicalId\":162052,\"journal\":{\"name\":\"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology\",\"volume\":\"159 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIBCB.2009.4925702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBCB.2009.4925702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance prediction for RNA design using parametric and non-parametric regression models
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 perproblem 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.