| Reference: | Akay, M.F., (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36 (2), 3240–3247.
Arunasakthi, K., KamatchiPriya, & L., Askerunisa, A., (2014). Fisher Score Dimensionality Reduction for SVM Classification. 2014 International Conference on Innovations in Engineering and Technology, 3(3), 1900-1904.
Blum, A., Langley, P., Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1-2), 245-271.
Bennasar, M., Hicks, Y., & Setchi, R., (2015). Feature selection using Joint Mutual Information Maximisation. Expert Systems with Applications, 42, 8520-8532.
Boser, B.E., Guyon, I.M., & Vapnil, V.N., (1992). A Training Algorithm for Optimal Margin Classiers. Proceedings of the fifth annual workshop on Computational learning theory, 144-152.
Breiman, L., (1996). Bagging Predictors. Machine Learning, 24, 123–140.
Breiman, L., (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324
Chen, Y.W., & Lin, C.J., (2005). Combining SVMs with Various Feature Selection Strategies. Department of Computer Science, National Taiwan University.
https://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf
Cortes, C., & Vapnik, V., (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018
Dash, M., & Liu, H., (1997). Feature selection for classification. Intelligent Data Analysis, 1(1-4), 131-156.
Díaz-Uriarte, R., & Alvarez de Andrés, S., (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7(1), 3.
doi:10.1186/1471-2105-7-3
Guyon, I., & Elisseeff, A., (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(March), 1157-1182.
Haldurai, H., Madhubala, T., & Rajalakshmi, R., (2016). A Study on Genetic Algorithm and its Applications. International Journal of Computer Sciences and Engineering, 4(10), 139-143.
Ho, T.K., (1995). Random decision forests. Proceedings of the Third International Conference on Document Analysis and Recognition, 1, 278.
Ho, T.K., (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832-844.
Holland, J.H., (1992). Genetic Algorithms. Scientific American, 267(1), 66-73.
Hsieh, F., Liu, S.Y., Hsieh, Y.C., McCowan, B., (2018). From patterned response
dependency to structured covariate dependency: Entropy based categorical-
pattern-matching. PLoS ONE, 13(6).
Hsu, C.W., Chang, C.C., & Lin, C.J., (2003). Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis. Department of Computer Science, National Taiwan University. https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Huang, C.L., & Wang, C.J., (2006). A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications, 31, 231-240.
Jolliffe, I.T., & Cadima, J., (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences, 374(2065), 20150202.
Kumbhar, P., & Mali, M., (2016). A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification. International Journal of Science and Research, 5(5), 1267-1275.
Popovic, M., (2018). Researchers in an Entropy Wonderland: A Review of the Entropy Concept. Thermal Science, 22(2), 1163-1178.
Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., & Jain, A.K., (2000). Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(2), 164-171.
Kohavi, R., John, G., (1997). Wrappers for feature selection. Artificial Intelligence, 97(1-2), 273-324.
Genuer, R., Poggi, J. M., & Tuleau-Malot, C., (2010). Variable selection using Random Forests. Pattern Recognition Letters, 31(14), 2225-2236.
Saeys, Y., Inza, I., & Larrañaga, P., (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507-2517.
Scho ̈lkopf, B., Sung, K.K., Burges, Chris J.C., Girosi, F., Niyogi, P., Poggio, T., & Vapnik, V., (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing, 45(11), 2758-2765.
Shannon, C. E., (1948). A Mathematical Theory of Communication. The Bell System Technical Journal. 27, 379-432, 623-656.
Srivastava, S., Joshi, N., & Gaur, M., (2013). A Review Paper on Feature Selection Methodologies and Their Applications. International Journal of Engineering Research and Development, 7(6), 57-61.
Tibshirani, R., (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267-288.
Xu, X., (2006). Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier Construction and Sequential Pattern Prediction. International Journal of Web Services Practices, 2(1-2), 49-58.
Yu, L., & Liu, H., (2003). Feature selection for high-dimensional data: A fast correlation based filter solution. Department of Computer Science & Engineering, Arizona State University. |
