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41. Hira ZM, Gillies DF (2015) A review of feature selection and feature extraction methods
applied on microarray data. Adv Bioinformatics. https://doi.org/10.1155/2015/198363
42. Rajarshi G, Jurs PC (2004) Development of linear, ensemble, and nonlinear models for the
prediction and interpretation of the biological activity of a set of PDGFR inhibitors. J Chem
Inf Comput Sci 44(6):2179–2189
43. Guo G, Neagu D, Cronin MTD (2005) A study on feature selection for toxicity prediction. In:
Wang L, Jin Y (eds) Fuzzy systems and knowledge discovery. Springer, Heidelberg, pp 31–34
44. Newby D, Freitas AA, Ghafourian T (2012) Pre-processing feature selection for improved
C&RT models for oral absorption. J Chem Inf Model 53(10):2730–2742
45. Pudil P, Novoviˇ cová J, Kittler J (1994) Floating search methods in feature selection. Pattern
Recognit Lett 15(11):1119–1125
46. Brendel M, Zaccarelli R, Devillers L (2010) A quick sequential forward floating feature selection algorithm for emotion detection from speech. In: INTERSPEECH-2010, Chiba, 26–30
September 2010
47. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of
ICNN’95—international conference on neural networks, Perth, 27 November–1 December
1995
48. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning.
Addison-Wesley Longman Publishing Co., Inc, Boston
49. Revathy N, Balasubramanian R (2012) GA-SVM Wrapper approach for gene banking and
classificaiton using expressions of very few genes. J Theor Appl Inf Technol 40(2):113–119
50. Shen Q, Jiang J-H, Tao J et al (2005) Modified ant colony optimization algorithm for variable
selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. J Chem Inf Model
45(4):1024–1029
51. Jain D, Singh V (2018) Feature selection and classification systems for chronic disease prediction: a review. Egypt Informatics J 19(3):179–189
52. Osman H, Ghafari M, Nierstrasz O (2017) Automatic feature selection by regularization to
improve bug prediction accuracy. In: 2017 IEEE workshop on machine learning techniques for
software quality evaluation (MaLTeSQuE), Klagenfurt, 21 February 2017
53. Reddy AS, Kumar S, Garg R (2010) Hybrid-genetic algorithm based descriptor optimization
and QSAR models for predicting the biological activity of tipranavir analogs for HIV protease
inhibition. J Mol Graph Model 28(8):852–862
54. Dutta D, Guha R, Wild D, Chen T (2007) Ensemble feature selection: consistent descriptor
subsets for multiple QSAR models. J Chem Inf Model 47(3):989–997
55. Zhu X-W, Xin Y-J, Ge H-L (2015) Recursive random forests enable better predictive performance and model interpretation than variable selection by LASSO. J Chem Inf Model
55(4):736–746
56. Lauria A, Ippolito M, Almerico AM. (2009) Combined use of PCA and QSAR/QSPR to predict
the drugs mechanism of action. An application to the NCI ACAM database. QSAR Comb Sci
28(4):387–395
57. Yoo C, Shahlaei M (2018) The applications of PCA in QSAR studies: a case study on CCR5
antagonists. Chem Biol Drug Des 91(1):137–152
58. Klepsch F, Vasanthanathan P, Ecker GF (2014) Ligand and structure-based classification models
for prediction of P-glycoprotein inhibitors. J Chem Inf Model 54(1):218–229
59. Hemmateenejad B, Miri R, Jafarpour M, Tabarzad M, Foroumadi A (2006) Multiple linear
regression and principal component analysis-based prediction of the anti-tuberculosis activity
of some 2-aryl-1,3,4-Thiadiazole derivatives. QSAR Comb Sci 25(1):56–66
60. Manikandan G, Abirami S (2018) A survey on feature selection and extraction techniques for
high-dimensional microarray datasets. In: Anouncia SM, Wiil UK (eds) Knowledge computing
and its applications. Springer, Singapore, pp 311–333
61. Reverter F, Vegas E, Oller JM (2014) Kernel-PCA data integration with enhanced interpretability. BMC Syst Biol 8(2):S6
62. Wang Q (2012) Kernel principal component analysis and its applications in face recognition
and active shape models. https://arxiv.org/abs/1207.3538. Accessed 10 October 2018
G. Idakwo et al.
41. Hira ZM, Gillies DF (2015) A review of feature selection and feature extraction methods
applied on microarray data. Adv Bioinformatics. https://doi.org/10.1155/2015/198363
42. Rajarshi G, Jurs PC (2004) Development of linear, ensemble, and nonlinear models for the
prediction and interpretation of the biological activity of a set of PDGFR inhibitors. J Chem
Inf Comput Sci 44(6):2179–2189
43. Guo G, Neagu D, Cronin MTD (2005) A study on feature selection for toxicity prediction. In:
Wang L, Jin Y (eds) Fuzzy systems and knowledge discovery. Springer, Heidelberg, pp 31–34
44. Newby D, Freitas AA, Ghafourian T (2012) Pre-processing feature selection for improved
C&RT models for oral absorption. J Chem Inf Model 53(10):2730–2742
45. Pudil P, Novoviˇ cová J, Kittler J (1994) Floating search methods in feature selection. Pattern
Recognit Lett 15(11):1119–1125
46. Brendel M, Zaccarelli R, Devillers L (2010) A quick sequential forward floating feature selection algorithm for emotion detection from speech. In: INTERSPEECH-2010, Chiba, 26–30
September 2010
47. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of
ICNN’95—international conference on neural networks, Perth, 27 November–1 December
1995
48. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning.
Addison-Wesley Longman Publishing Co., Inc, Boston
49. Revathy N, Balasubramanian R (2012) GA-SVM Wrapper approach for gene banking and
classificaiton using expressions of very few genes. J Theor Appl Inf Technol 40(2):113–119
50. Shen Q, Jiang J-H, Tao J et al (2005) Modified ant colony optimization algorithm for variable
selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. J Chem Inf Model
45(4):1024–1029
51. Jain D, Singh V (2018) Feature selection and classification systems for chronic disease prediction: a review. Egypt Informatics J 19(3):179–189
52. Osman H, Ghafari M, Nierstrasz O (2017) Automatic feature selection by regularization to
improve bug prediction accuracy. In: 2017 IEEE workshop on machine learning techniques for
software quality evaluation (MaLTeSQuE), Klagenfurt, 21 February 2017
53. Reddy AS, Kumar S, Garg R (2010) Hybrid-genetic algorithm based descriptor optimization
and QSAR models for predicting the biological activity of tipranavir analogs for HIV protease
inhibition. J Mol Graph Model 28(8):852–862
54. Dutta D, Guha R, Wild D, Chen T (2007) Ensemble feature selection: consistent descriptor
subsets for multiple QSAR models. J Chem Inf Model 47(3):989–997
55. Zhu X-W, Xin Y-J, Ge H-L (2015) Recursive random forests enable better predictive performance and model interpretation than variable selection by LASSO. J Chem Inf Model
55(4):736–746
56. Lauria A, Ippolito M, Almerico AM. (2009) Combined use of PCA and QSAR/QSPR to predict
the drugs mechanism of action. An application to the NCI ACAM database. QSAR Comb Sci
28(4):387–395
57. Yoo C, Shahlaei M (2018) The applications of PCA in QSAR studies: a case study on CCR5
antagonists. Chem Biol Drug Des 91(1):137–152
58. Klepsch F, Vasanthanathan P, Ecker GF (2014) Ligand and structure-based classification models
for prediction of P-glycoprotein inhibitors. J Chem Inf Model 54(1):218–229
59. Hemmateenejad B, Miri R, Jafarpour M, Tabarzad M, Foroumadi A (2006) Multiple linear
regression and principal component analysis-based prediction of the anti-tuberculosis activity
of some 2-aryl-1,3,4-Thiadiazole derivatives. QSAR Comb Sci 25(1):56–66
60. Manikandan G, Abirami S (2018) A survey on feature selection and extraction techniques for
high-dimensional microarray datasets. In: Anouncia SM, Wiil UK (eds) Knowledge computing
and its applications. Springer, Singapore, pp 311–333
61. Reverter F, Vegas E, Oller JM (2014) Kernel-PCA data integration with enhanced interpretability. BMC Syst Biol 8(2):S6
62. Wang Q (2012) Kernel principal component analysis and its applications in face recognition
and active shape models. https://arxiv.org/abs/1207.3538. Accessed 10 October 2018