Abstract
A neuro-fuzzy expert system is multi-objective, which hybrids Ant Bee Colony (ABC) with Adaptive Neuro-Fuzzy Inference System (ANFIS) called NF-ABC, which improves the classification accuracy and reduces the complexity of dimensionality, redundancy, and irrelevant data. In this proposed work, SVM and kNN algorithms are used for classification to classify the given micro array data. The results revealed that the proposed model is more successful than the previous model.
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J. Li, P. Duan, H. Sang, S. Wang, Z. Liu, P. Duan, An efficient optimization algorithm for resource-constrained steelmaking scheduling problems. IEEE Access 6, 33883–33894 (2018)
Chabaa S et al., Application of adaptive neuro-fuzzy inference systems for analyzing non-gaussian signal, in 2009 International Conference on Multimedia Computing and Systems (IEEE Explore)
S.M. Odeh, Using an adaptive neuro-fuzzy inference system (AnFis) algorithm for automatic diagnosis of skin cancer. J. Commun. Computer 8, 751–755 (2011)
Y. Marinakis, A hybrid ACO-GRASP algorithm for clustering analysis. Ann. Oper. Res 188(1), 343–358 (2011)
P. Ganesh Kumar, C. Rani, D. Devaraj, A. Albert Victorie, Hybrid Ant Bee algorithm for fuzzy expert system based sample classification. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(2), 347–360 (2014)
H. Shah, R. Ghazali, N. Mohd Nawi, Hybrid Ant Bee Colony Algorithm for Volcano Temperature Prediction (Springer, Berlin, 2012), pp. 453–465
J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing (Prentice-Hall, Upper Saddle River, NJ, 1997)
X. Zong, Z. Yong, J. Li-Min, H. Wei-Li, Construct interpretable fuzzy classification system based on fuzzy clustering initialization. Int. J. Inform. Technol. 11(6), 91–107 (2005)
P. Woolf, Y. Wang, A fuzzy logic approach to analyzing gene expression data. Physiol. Genomics 3, 9–15 (2000)
S. Vinterbo, Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 21(9), 1964–1970 (2005)
A.C. Tan, D. Gilbert, Ensemble machine learning on gene expression data for cancer classification. Appl. Bioinform. 2, 75–83 (2003)
S. Haddou Bouazza, N. Hamdi, A. Zeroual, Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers, in 2015 Intelligent Systems and Computer Vision (ISCV), vol. 1 (IEEE), pp. 1–6. https://www.computer.org/csdl/proceedings-article/iscv/2015/07106168/12OmNzCWG8q
T.S. Furey, N. Cristianini, N. Duffy, D.W. Bednarski, M. Schummer, D. Haussler, Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906–914 (2000)
F. Chu, L. Wang, Applications of support vector machines to cancer classification with microarray data. Int. J. Neural Syst. 15(06), 475–484 (2005)
G. Schaefer, Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recogn. 42(6), 1133–1137 (2009)
UCI machine learning repository, http://www.archive.ics.uci.edu/ml/
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Nancy, S.G., Saranya, K., Rajasekar, S. (2020). Neuro-Fuzzy Ant Bee Colony Based Feature Selection for Cancer Classification. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_4
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DOI: https://doi.org/10.1007/978-3-030-19562-5_4
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