Editing Feature selection (section)

===Application of feature selection metaheuristics===
This is a survey of the application of feature selection metaheuristics lately used in the literature. This survey was realized by J. Hammon in her 2013 thesis.<ref name="ReferenceA"/>

{| class="wikitable sortable"
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! Application !! Algorithm !! Approach !! Classifier !! [[Evaluation function|Evaluation Function]] !! Reference
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| [[Single-nucleotide polymorphism|SNPs]] || Feature Selection using Feature Similarity || Filter ||   || r<sup>2</sup> || Phuong 2005<ref name="M. Phuong, Z pages 301-309"/>
|-
| SNPs || [[Genetic algorithm]] || Wrapper || [[Decision tree learning|Decision Tree]] || Classification accuracy (10-fold) || Shah 2004<ref>{{cite journal | last1 = Shah | first1 = S. C. | last2 = Kusiak | first2 = A. | year = 2004 | title = Data mining and genetic algorithm based gene/SNP selection | journal = Artificial Intelligence in Medicine | volume = 31 | issue = 3| pages = 183–196 | doi = 10.1016/j.artmed.2004.04.002 | pmid = 15302085 }}</ref>
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| SNPs || [[Hill climbing]] || Filter + Wrapper || [[Naive Bayes classifier|Naive Bayesian]] || Predicted residual sum of squares || Long 2007<ref>{{cite journal | last1 = Long | first1 = N. | last2 = Gianola | first2 = D. | last3 = Weigel | first3 = K. A | year = 2011 | title = Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins | journal = Journal of Animal Breeding and Genetics | volume = 128 | issue = 4| pages = 247–257 | doi=10.1111/j.1439-0388.2011.00917.x| pmid = 21749471 }}</ref>
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| SNPs || [[Simulated annealing]] ||  || Naive bayesian || Classification accuracy (5-fold) || Ustunkar 2011<ref>{{Cite journal |doi = 10.1007/s11590-011-0419-7|title = Selection of representative SNP sets for genome-wide association studies: A metaheuristic approach|journal = Optimization Letters|volume = 6|issue = 6|pages = 1207–1218|year = 2012|last1 = Üstünkar|first1 = Gürkan|last2 = Özöğür-Akyüz|first2 = Süreyya|last3 = Weber|first3 = Gerhard W.|last4 = Friedrich|first4 = Christoph M.|last5 = Aydın Son|first5 = Yeşim|s2cid = 8075318}}</ref>
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| Segments parole || [[Ant colony optimization algorithms|Ant colony]] || Wrapper || [[Artificial Neural Network]] || [[Mean squared error|MSE]] || Al-ani 2005 {{citation needed|reason=Need RS|date=March 2016}}
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| Marketing || Simulated annealing || Wrapper || Regression || [[Akaike information criterion|AIC]], r<sup>2</sup> || Meiri 2006<ref>{{cite journal |first1=R. |last1=Meiri |first2=J. |last2=Zahavi |title=Using simulated annealing to optimize the feature selection problem in marketing applications |journal=European Journal of Operational Research |volume=171 |issue=3 |pages=842–858 |date=2006 |doi=10.1016/j.ejor.2004.09.010 }}</ref>
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| Economics || Simulated annealing, genetic algorithm || Wrapper || Regression || [[Bayesian information criterion|BIC]] || Kapetanios 2007<ref>{{cite journal |first=G. |last=Kapetanios |title=Variable Selection in Regression Models using Nonstandard Optimisation of Information Criteria |journal=Computational Statistics & Data Analysis |volume=52 |issue=1 |year=2007 |pages=4–15 |doi=10.1016/j.csda.2007.04.006 }}</ref>
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| Spectral Mass || Genetic algorithm || Wrapper || Multiple Linear Regression, [[Partial least squares regression|Partial Least Squares]] || [[root-mean-square error]] of prediction || Broadhurst et al. 1997<ref>{{cite journal |first1=D. |last1=Broadhurst |first2=R. |last2=Goodacre |first3=A. |last3=Jones |first4=J. J. |last4=Rowland |first5=D. B. |last5=Kell |title=Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry |journal=Analytica Chimica Acta |volume=348 |issue=1–3 |pages=71–86 |year=1997 |doi=10.1016/S0003-2670(97)00065-2 |bibcode=1997AcAC..348...71B }}</ref>
|-
| Spam || [[Particle swarm optimization#Binary, discrete, and combinatorial|Binary PSO]] + [[Mutation (genetic algorithm)|Mutation]] || Wrapper || [[Decision tree]] || weighted cost || Zhang 2014<ref name="sciencedirect.com"/> 
|-
| Microarray || [[Tabu search]] + [[Particle swarm optimization|PSO]] || Wrapper || [[Support Vector Machine]], [[k-nearest neighbors algorithm|K Nearest Neighbors]] || [[Euclidean Distance]]  || Chuang 2009<ref>{{cite journal | last1 = Chuang | first1 = L.-Y. | last2 = Yang | first2 = C.-H. | year = 2009 | title = Tabu search and binary particle swarm optimization for feature selection using microarray data | journal = Journal of Computational Biology | volume = 16 | issue = 12| pages = 1689–1703 | doi = 10.1089/cmb.2007.0211 | pmid = 20047491 }}</ref>
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| Microarray || PSO + Genetic algorithm || Wrapper || Support Vector Machine || Classification accuracy (10-fold) || Alba 2007<ref>E. Alba, J. Garia-Nieto, L. Jourdan et E.-G. Talbi. [http://neo.lcc.uma.es/presentacionesCongreso/JMcec2007.pdf Gene Selection in Cancer Classification using PSO-SVM and GA-SVM Hybrid Algorithms.] {{Webarchive|url=https://web.archive.org/web/20160818135718/http://neo.lcc.uma.es/presentacionesCongreso/JMcec2007.pdf |date=2016-08-18 }} Congress on Evolutionary Computation, Singapore: Singapore (2007), 2007</ref>
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| Microarray || Genetic algorithm + [[Iterated local search|Iterated Local Search]] || Embedded || Support Vector Machine || Classification accuracy (10-fold) || Duval 2009<ref name="B. Duval, J pages 201-208">B. Duval, J.-K. Hao et J. C. Hernandez Hernandez. [http://www.info.univ-angers.fr/pub/hao/papers/GECCO09.pdf A memetic algorithm for gene selection and molecular classification of an cancer.] In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO '09, pages 201-208, New York, NY, USA, 2009. ACM.</ref>
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| Microarray || Iterated local search || Wrapper || Regression || [[Posterior probability|Posterior Probability]] || Hans 2007<ref>C. Hans, A. Dobra et M. West. [https://www.researchgate.net/profile/Adrian_Dobra/publication/228388856_Shotgun_Stochastic_Search_for_Large_p_Regression/links/02bfe5125185997d06000000.pdf Shotgun stochastic search for 'large p' regression]. Journal of the American Statistical Association, 2007.</ref>
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| Microarray || Genetic algorithm || Wrapper || K Nearest Neighbors || Classification accuracy ([[LOOCV|Leave-one-out cross-validation]]) || Jirapech-Umpai 2005<ref>{{cite journal | last1 = Aitken | first1 = S. | year = 2005 | title = Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes | doi = 10.1186/1471-2105-6-148 | pmid = 15958165 | pmc = 1181625 | journal = BMC Bioinformatics | volume = 6 | issue = 1| page = 148 | doi-access = free }}</ref>
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| Microarray || [[Memetic algorithm|Hybrid genetic algorithm]] || Wrapper || K Nearest Neighbors || Classification accuracy (Leave-one-out cross-validation) || Oh 2004<ref>{{cite journal | last1 = Oh | first1 = I. S. | last2 = Moon | first2 = B. R. | year = 2004 | title = Hybrid genetic algorithms for feature selection | journal = [[IEEE Transactions on Pattern Analysis and Machine Intelligence]] | volume = 26 | issue = 11| pages = 1424–1437 | doi=10.1109/tpami.2004.105| pmid = 15521491 | citeseerx = 10.1.1.467.4179 }}</ref>
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| Microarray || Genetic algorithm || Wrapper || Support Vector Machine || [[Sensitivity and specificity]] || Xuan 2011<ref>{{cite journal | last1 = Xuan | first1 = P. | last2 = Guo | first2 = M. Z. | last3 = Wang | first3 = J. | last4 = Liu | first4 = X. Y. | last5 = Liu | first5 = Y. | year = 2011 | title = Genetic algorithm-based efficient feature selection for classification of pre-miRNAs | journal = Genetics and Molecular Research | volume = 10 | issue = 2| pages = 588–603 | doi = 10.4238/vol10-2gmr969 | pmid = 21491369 | doi-access = free }}</ref>
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| Microarray || Genetic algorithm || Wrapper || All paired Support Vector Machine || Classification accuracy (Leave-one-out cross-validation) || Peng 2003<ref>{{cite journal | last1 = Peng | first1 = S. | year = 2003 | title = Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines | journal = FEBS Letters | volume = 555 | issue = 2| pages = 358–362 | doi=10.1016/s0014-5793(03)01275-4| pmid = 14644442 | doi-access = free | bibcode = 2003FEBSL.555..358P }}</ref>
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| Microarray || Genetic algorithm || Embedded || Support Vector Machine || Classification accuracy (10-fold) || Hernandez 2007<ref>{{cite book |first1=J. C. H. |last1=Hernandez |first2=B. |last2=Duval |first3=J.-K. |last3=Hao |chapter=A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data |title=Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007 |series=Lecture Notes in Computer Science |volume=4447 |pages=90–101 |location=Berlin |year=2007 |publisher=Springer Verlag |isbn=978-3-540-71782-9 |doi=10.1007/978-3-540-71783-6_9 }}</ref>
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| Microarray || Genetic algorithm || Hybrid || Support Vector Machine || Classification accuracy (Leave-one-out cross-validation) || Huerta 2006<ref>{{cite book |first1=E. B. |last1=Huerta |first2=B. |last2=Duval |first3=J.-K. |last3=Hao |chapter=A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data |title=Applications of Evolutionary Computing. EvoWorkshops 2006 |series=Lecture Notes in Computer Science |volume=3907 |pages=34–44 |year=2006 |isbn=978-3-540-33237-4 |doi=10.1007/11732242_4 }}</ref>
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| Microarray || Genetic algorithm ||  || Support Vector Machine || Classification accuracy (10-fold) || Muni 2006<ref>{{cite journal |first1=D. P. |last1=Muni |first2=N. R. |last2=Pal |first3=J. |last3=Das |title=Genetic programming for simultaneous feature selection and classifier design |journal= IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics|volume=36 |issue=1 |pages=106–117 |year=2006 |doi=10.1109/TSMCB.2005.854499 |pmid=16468570 |s2cid=2073035 }}</ref>
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| Microarray || Genetic algorithm || Wrapper || Support Vector Machine || EH-DIALL, CLUMP || Jourdan 2005<ref>{{cite journal |first1=L. |last1=Jourdan |first2=C. |last2=Dhaenens |first3=E.-G. |last3=Talbi |title=Linkage disequilibrium study with a parallel adaptive GA |journal=[[International Journal of Foundations of Computer Science]] |year=2005 |volume=16 |issue=2 |pages=241–260 |doi=10.1142/S0129054105002978 }}</ref>
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|[[Alzheimer's disease]] || [[Welch's t-test]] || Filter || Support vector machine || Classification accuracy (10-fold) || Zhang 2015<ref>{{cite journal|last1=Zhang|first1=Y.|last2=Dong|first2=Z.|last3=Phillips|first3=P.|last4=Wang|first4=S.|title=Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning|journal=Frontiers in Computational Neuroscience|date=2015|volume=9|page=66|doi=10.3389/fncom.2015.00066|pmid=26082713|pmc=4451357|doi-access=free}}</ref>
|-
|[[Computer vision]]
|[https://it.mathworks.com/matlabcentral/fileexchange/56937-feature-selection-library Infinite Feature Selection]
|Filter
|Independent
|[[Average Precision]], [[ROC curve#Area under the curve|ROC AUC]]
|Roffo 2015<ref>{{Cite book|last1=Roffo|first1=G.|last2=Melzi|first2=S.|last3=Cristani|first3=M.|title=2015 IEEE International Conference on Computer Vision (ICCV) |chapter=Infinite Feature Selection |date=2015-12-01|pages=4202–4210|doi=10.1109/ICCV.2015.478|isbn=978-1-4673-8391-2|s2cid=3223980}}</ref>
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|Microarrays
|[https://it.mathworks.com/matlabcentral/fileexchange/56937-feature-selection-library Eigenvector Centrality FS]
|Filter
|Independent
|Average Precision, Accuracy, ROC AUC
|Roffo & Melzi 2016<ref>{{Cite web|url=http://www.di.uniba.it/~loglisci/NFmcp2016/NFmcp2016_paper_13.pdf|title=Features Selection via Eigenvector Centrality|last1=Roffo|first1=Giorgio|last2=Melzi|first2=Simone|date=September 2016|publisher=NFmcp2016|access-date=12 November 2016}}</ref>
|-
|XML
| Symmetrical Tau (ST) 
|Filter
| Structural Associative Classification 
|Accuracy, Coverage 
|Shaharanee & Hadzic 2014
|}