DWFS: A wrapper feature selection tool based on a parallel Genetic Algorithm

PLoS ONE

Volumn 10, Issue 2, 2015, Pages

  Soufan, Othman ^a   Kleftogiannis, Dimitrios ^a   Kalnis, Panos ^a   Bajic, Vladimir B ^a  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; DWFS WEB; GENE MUTATION; GENETIC ALGORITHM; GENETIC ANALYSIS; INTERNET; MICROARRAY ANALYSIS; NEXT GENERATION SEQUENCING; ONLINE SYSTEM; POPULATION SIZE; PREDICTION; SENSITIVITY AND SPECIFICITY; SKILL; TASK PERFORMANCE; WEB BROWSER; DATA MINING; GENETIC DATABASE; GENOMICS; PROCEDURES; SELECTION BIAS; SOFTWARE;

DATA MINING; DATABASES, GENETIC; GENOMICS; SELECTION BIAS; SOFTWARE;

EID: 84923828257     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0117988     Document Type: Article

References (41)

1. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. The Journal of Machine Learning Research 3: 1157-1182.
2. Garbarine E, DePasquale J, Gadia V, Polikar R, Rosen G (2011) Information-theoretic approaches to SVM feature selection for metagenome read classification. Computational biology and chemistry 35: 199-209. doi: 10.1016/j.compbiolchem.2011.04.007 PMID: 21704267
3. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. Knowledge and Data Engineering, IEEE Transactions on 17: 491-502.
4. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. bioinformatics 23: 2507-2517. PMID: 17720704
5. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artificial intelligence 97: 273-324.
6. Holland JH (1975) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence: U Michigan Press. PMID: 25077283
7. Wang H, Lo S-H, Zheng T, Hu I (2012) Interaction-based feature selection and classification for high-dimensional biological data. bioinformatics 28: 2834-2842. doi: 10.1093/bioinformatics/bts531 PMID: 22945786
8. Somol P, Grim J, Pudil P (2010) The problem of fragile feature subset preference in feature selection methods and a proposal of algorithmic workaround. IEEE. pp. 4396-4399. doi: 10.1016/j.febslet.2010. 10.013 PMID: 20951136
9. Rokach L (2008) Data mining with decision trees: theory and applications: World scientific. PMID: 25506952
10. Duval B, Hao J-K (2010) Advances in metaheuristics for gene selection and classification of microarray data. Briefings in Bioinformatics 11: 127-141. doi: 10.1093/bib/bbp035 PMID: 19789265
11. Hilario M, Kalousis A (2008) Approaches to dimensionality reduction in proteomic biomarker studies. Briefings in Bioinformatics 9: 102-118. doi: 10.1093/bib/bbn005 PMID: 18310106
12. Somol P, Vácha P, Mikeš S, Hora J, Pudil P, et al. (2010) Introduction to Feature Selection Toolbox 3-The C++ Library for Subset Search, Data Modeling and Classification. Research Report for Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic.
13. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, et al. (2009) The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 11: 10-18.
14. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. Pattern Analysis and Machine Intelligence, IEEE Transactions on 27: 1226-1238.
15. Cheng T, Wang Y, Bryant SH (2012) FSelector: a Ruby gem for feature selection. bioinformatics 28: 2851-2852. doi: 10.1093/bioinformatics/bts528 PMID: 22942017
16. Seo M, Oh S (2012) CBFS: High performance feature selection algorithm based on feature clearness. PloS one 7: e40419. doi: 10.1371/journal.pone.0040419 PMID: 22792310
17. Glaab E, Garibaldi JM, Krasnogor N (2009) ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization. BMC bioinformatics 10: 358. doi: 10.1186/1471-2105-10-358 PMID: 19863798
18. Ye K, Feenstra KA, Heringa J, IJzerman AP, Marchiori E (2008) Multi-RELIEF: a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting. bioinformatics 24: 18-25. PMID: 18024975
19. Xia J, Psychogios N, Young N, Wishart DS (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic acids research 37: W652-W660. doi: 10.1093/nar/gkp356 PMID: 19429898
20. Levine D (1996) Users guide to the PGAPack parallel genetic algorithm library. Argonne National Laboratory 9700. PMID: 8703941
21. Cantú-Paz E (1998) A survey of parallel genetic algorithms. Calculateurs paralleles, reseaux et systems repartis 10: 141-171.
22. Siedlecki W, Sklansky J (1989) A note on genetic algorithms for large-scale feature selection. Pattern recognition letters 10: 335-347.
23. Akbani R, Kwek S, Japkowicz N (2004) Applying support vector machines to imbalanced datasets. Machine Learning: ECML 2004: Springer. pp. 39-50.
24. Bochkanov S (2010) ALGLIB software library, L-BFGS C++ implementation.
25. Yang HH, Moody JE (1999) Data Visualization and Feature Selection: New Algorithms for Nongaussian Data. Citeseer. pp. 687-702. doi: 10.1094/PHYTO.1999.89.8.687 PMID: 18944682
26. Fleuret F (2004) Fast binary feature selection with conditional mutual information. The Journal of Machine Learning Research 5: 1531-1555.
27. Jakulin A (2005) Machine learning based on attribute interactions: Univerza v Ljubljani. PMID: 25275211
28. Brown G, Pocock A, Zhao M-J, Luján M (2012) Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. The Journal of Machine Learning Research 13: 27-66.
29. Cawley GC, Talbot NL (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. The Journal of Machine Learning Research 11: 2079-2107.
30. Braga-Neto UM, Dougherty ER (2004) Is cross-validation valid for small-sample microarray classification? bioinformatics 20: 374-380. PMID: 14960464
31. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. pp. 1137-1145.
32. Schmeier S, Jankovic B, Bajic VB (2011) Simplified method to predict mutual interactions of human transcription factors based on their primary structure. PloS one 6: e21887. doi: 10.1371/journal.pone. 0021887 PMID: 21750739
33. Lustgarten JL, Gopalakrishnan V, Visweswaran S (2009) Measuring stability of feature selection in biomedical datasets American Medical Informatics Association. pp. 406. doi: 10.1111/j.1551-6709.2009. 01079.x PMID: 21564219
34. Bajić VB (2000) Comparing the success of different prediction software in sequence analysis: a review. Briefings in Bioinformatics 1: 214-228. PMID: 11465033
35. Zare H (2011) FeaLect: Feature seLection by computing statistical scores.
36. Magana-Mora A, Ashoor H, Jankovic BR, Kamau A, Awara K, et al. (2013) Dragon TIS Spotter: an Ara-bidopsis-derived predictor of translation initiation sites in plants. bioinformatics 29: 117-118. doi: 10. 1093/bioinformatics/bts638 PMID: 23110968
37. Blake C, Merz CJ (1998) {UCI} Repository of machine learning databases.
38. Batuwita R, Palade V (2009) microPred: effective classification of pre-miRNAs for human miRNA gene prediction. bioinformatics 25: 989-995. doi: 10.1093/bioinformatics/btp107 PMID: 19233894
39. Spira A, Beane JE, Shah V, Steiling K, Liu G, et al. (2007) Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nature medicine 13: 361-366. PMID: 17334370
40. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. science 286: 531-537. PMID: 10521349
41. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, et al. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer cell 1: 203-209. PMID: 12086878