Hybrid (generalization-correlation) method for feature selection in high dimensional DNA microarray prediction problems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6692 LNCS, Issue PART 2, 2011, Pages 202-209

  Couce, Yasel ^a   Franco, Leonardo ^b   Urda, Daniel ^b   Subirats, José L ^b   Jerez, José M ^b  

^a UNIVERSIDAD DE LAS CIENCIAS INFORMÁTICAS   (Cuba)

^b UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Constructive Neural Networks; Data Mining; DNA Microarray; Feature Selection

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPUTATIONAL COSTS; CONSTRUCTIVE NEURAL NETWORK; DNA MICRO-ARRAY; FEATURE SELECTION; FILTER METHOD; HIGH-DIMENSIONAL; HYBRID MODEL; MACHINE LEARNING METHODS; MICROARRAY DATA ANALYSIS; PREDICTION ACCURACY; PREDICTION PROBLEM; WRAPPER METHODS;

DATA REDUCTION; FEATURE EXTRACTION; FILTRATION; FORECASTING; LEARNING SYSTEMS; NUCLEIC ACIDS; ARTIFICIAL INTELLIGENCE; DATA MINING; NEURAL NETWORKS;

NEURAL NETWORKS; FEATURE EXTRACTION;

EID: 79957944585     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21498-1_26     Document Type: Conference Paper

References (10)

1. Huda, S., Yearwood, J., Strainieri, A.: Hybrid Wrapper-Filter Approaches for Input Feature Selection Using Maximum Relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA). In: 4th International Conference on Network and System Security, pp. 442-449 (2010)
2. Inza, I., Larrañaga, P., Blanco, R., Cerrolaza, A.J.: Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence in Medicine 31, 91-103 (2004)
3. Kohavi, R., John, G.: The Wrapper approach. In: Feature Extraction, Construction and Selection: a data mining perspective, pp. 33-51 (1998)
4. Lancashire, L.J., Rees, R.C., Ball, G.R.: Identification of gene transcript signatures predictive for er and lymph node status using a stepwise forward selection ann modelling approach. Artif. Intell. Med. 43, 99-111 (2008)
5. Pirooznia, M., Yang, J., Yang, M.Q., Deng, Y.: A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics 9, S13 (2008)
6. Sebban, M., Nock, R.: A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recognition 35, 835-846 (2002)
7. Subirats, J.L., Jerez, J.M., Gómez, I., Franco, L.: Multiclass Pattern Recognition Extension for the New C-Mantec Constructive Neural Network Algorithm. Cognitive Computation 2, 285-290 (2010)
8. Urda, D., Subirats, J.L., Franco, L., Jerez, J.M.: Constructive neural networks to predict breast cancer outcome by using gene expression profiles. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010. LNCS, vol. 6096, pp. 317-326. Springer, Heidelberg (2010)
9. West, M.: Bayesian factor regression models in the "large p, small n" paradigm. Bayesian statistics 7, 723-732 (2003)
10. West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson, J.A., Marks, J.R., Nevins, J.R.: Predicting the clinical status of human breast cancer by using gene expression profiles. PNAS 98, 11462-11467 (2001)