7 A Review of Feature Reduction Methods …
135
16. Clarke R, Ressom HW, Wang A et al (2008) The properties of high-dimensional data spaces:
implications for exploring gene and protein expression data. Nat Rev Cancer 8(1):37–49
17. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507
18. Ang JC, Mirzal A, Haron H, Hamed HNA (2016) Supervised, unsupervised, and semisupervised feature selection: a review on gene selection. IEEE/ACM Trans Comput Biol Bioinform 13(5):971–989
19. Merkwirth C, Mauser H, Schulz-Gasch T, Roche O, Martin Stahl A, Lengauer T (2004) Ensemble methods for classification in cheminformatics. J Chem Inf Comput Sci 44(6):1971–1978
20. Venkatraman V, Dalby AR, Yang ZR (2004) Evaluation of mutual information and genetic
programming for feature selection in QSAR. J Chem Inf Comput Sci 44(5):1686–1692
21. Bajorath J (2001) Selected concepts and investigations in compound classification, molecular
descriptor analysis, and virtual screening. J Chem Inf Comput Sci 41(2):233–245
22. Goodarzi M, Dejaegher B, Heyden YV (2012) Feature selection methods in QSAR studies. J
AOAC Int 95(3):636–651
23. Shahlaei M (2013) Descriptor selection methods in quantitative structure—activity relationship
studies: a review study. Chem Rev 113(10):8093–8103
24. Bellman R (2016) Adaptive control processes: a guided tour. Princeton University Press, New
Jersey
25. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng
40(1):16–28
26. Van Der Maaten L, Postma E, Van Den Herik J (2009) Dimensionality reduction: a comparative
review. J Mach Learn Res 10:66–71
27. Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective.
Neurocomputing 300:70–79
28. Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. In: Aggarwal
CC (ed) Data classification: algorithms and applications, 1st edn. CRC Press, Boca Raton, pp
37–64
29. Johnstone IM, Titterington DM (2009) Statistical challenges of high-dimensional data. Philos
Trans A Math Phys Eng Sci 367(1906):4237–4253
30. Zhu X, Wu X (2004) Class noise versus attribute noise: a quantitative study. Artif Intell Rev
22(3): 177 –210
31. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern
43(1):59–69
32. Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised
feature selection methods. Pattern Recognit 64:141–158
33. Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res
5:845–889
34. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn
Res 3:1157–1182
35. Solorio-Fernandez S, Martinez-Trinidad JF, Carrasco-Ochoa JA, and Zhang Y-Q (2012) Hybrid
feature selection method for biomedical datasets. In: 2012 IEEE symposium on computational
intelligence in bioinformatics and computational biology (CIBCB), San Diego, 9–12 May 2012
36. Hsu H-H, Hsieh C-W, Lu M-D (2011) Hybrid feature selection by combining filters and wrappers. Expert Syst Appl 38(7):8144–8150
37. Guan D, Yuan W, Lee YK, Najeebullah K, Rasel MK (2014) A review of ensemble learning
based feature selection. IETE Tech Rev 31(3):190–198
38. Brahim AB, Limam M (2017) Ensemble feature selection for high dimensional data: a new
method and a comparative study. Adv Data Anal Classif 12(4):937–952
39. Seijo-Pardo B, Porto-Díaz I, Bolón-Canedo V, Alonso-Betanzos A (2017) Ensemble feature
selection: homogeneous and heterogeneous approaches. Knowl Based Syst 118:124–139
40. Janecek A, Gansterer W, Demel M, Ecker G (2008) On the relationship between feature selection and classification accuracy. Proc Mach Learn Res 4:90–105
135
16. Clarke R, Ressom HW, Wang A et al (2008) The properties of high-dimensional data spaces:
implications for exploring gene and protein expression data. Nat Rev Cancer 8(1):37–49
17. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507
18. Ang JC, Mirzal A, Haron H, Hamed HNA (2016) Supervised, unsupervised, and semisupervised feature selection: a review on gene selection. IEEE/ACM Trans Comput Biol Bioinform 13(5):971–989
19. Merkwirth C, Mauser H, Schulz-Gasch T, Roche O, Martin Stahl A, Lengauer T (2004) Ensemble methods for classification in cheminformatics. J Chem Inf Comput Sci 44(6):1971–1978
20. Venkatraman V, Dalby AR, Yang ZR (2004) Evaluation of mutual information and genetic
programming for feature selection in QSAR. J Chem Inf Comput Sci 44(5):1686–1692
21. Bajorath J (2001) Selected concepts and investigations in compound classification, molecular
descriptor analysis, and virtual screening. J Chem Inf Comput Sci 41(2):233–245
22. Goodarzi M, Dejaegher B, Heyden YV (2012) Feature selection methods in QSAR studies. J
AOAC Int 95(3):636–651
23. Shahlaei M (2013) Descriptor selection methods in quantitative structure—activity relationship
studies: a review study. Chem Rev 113(10):8093–8103
24. Bellman R (2016) Adaptive control processes: a guided tour. Princeton University Press, New
Jersey
25. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng
40(1):16–28
26. Van Der Maaten L, Postma E, Van Den Herik J (2009) Dimensionality reduction: a comparative
review. J Mach Learn Res 10:66–71
27. Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective.
Neurocomputing 300:70–79
28. Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. In: Aggarwal
CC (ed) Data classification: algorithms and applications, 1st edn. CRC Press, Boca Raton, pp
37–64
29. Johnstone IM, Titterington DM (2009) Statistical challenges of high-dimensional data. Philos
Trans A Math Phys Eng Sci 367(1906):4237–4253
30. Zhu X, Wu X (2004) Class noise versus attribute noise: a quantitative study. Artif Intell Rev
22(3): 177 –210
31. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern
43(1):59–69
32. Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised
feature selection methods. Pattern Recognit 64:141–158
33. Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res
5:845–889
34. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn
Res 3:1157–1182
35. Solorio-Fernandez S, Martinez-Trinidad JF, Carrasco-Ochoa JA, and Zhang Y-Q (2012) Hybrid
feature selection method for biomedical datasets. In: 2012 IEEE symposium on computational
intelligence in bioinformatics and computational biology (CIBCB), San Diego, 9–12 May 2012
36. Hsu H-H, Hsieh C-W, Lu M-D (2011) Hybrid feature selection by combining filters and wrappers. Expert Syst Appl 38(7):8144–8150
37. Guan D, Yuan W, Lee YK, Najeebullah K, Rasel MK (2014) A review of ensemble learning
based feature selection. IETE Tech Rev 31(3):190–198
38. Brahim AB, Limam M (2017) Ensemble feature selection for high dimensional data: a new
method and a comparative study. Adv Data Anal Classif 12(4):937–952
39. Seijo-Pardo B, Porto-Díaz I, Bolón-Canedo V, Alonso-Betanzos A (2017) Ensemble feature
selection: homogeneous and heterogeneous approaches. Knowl Based Syst 118:124–139
40. Janecek A, Gansterer W, Demel M, Ecker G (2008) On the relationship between feature selection and classification accuracy. Proc Mach Learn Res 4:90–105