Abstract
Breast cancer is one of the most common tumors related to death in women in many countries. In this paper, a novel neural network classification model is developed. The proposed model uses floating centroids method and particle swarm optimization algorithm with inertia weight as optimizer to improve the performance of neural network classifier. Wisconsin breast cancer datasets in UCI Machine Learning Repository are tested with neural network classifier of the proposed method. Experimental results show that the developed model improves search convergence and performance. The accuracy of classification of benign and malignant tumors could be improved by the developed method compared with other classification techniques.
Keywords
- Floating Centroids Method
- PSO
- Neural Network
- Breast Cancer Classification
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References
Hulka, B.S., Moorman, P.G.: Breast Cancer: Hormones and Other Risk Factors. Maturitas 38(1), 103–113 (2001)
Corchado, E., Graña, M., Wozniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)
García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180(10), 2044–2064 (2010)
Corchado, E., Abraham, A., Carvalho, A.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)
Pedrycz, W., Aliev, R.: Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 73(1-3), 10–23 (2009)
Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)
Platt, J., Cristianini, N., Shawe Taylor, J.: Large Margin DAGs for Multiclass Classification. In: Advances in Neural Information Processing Systems, pp. 547–553 (2000)
Lu, H., Rudy, S., Huan, L.: Effect data mining using neural networks. IEEE Transcation Knowledge Data Engineer. 8(6), 957–961 (1996)
Misraa, B.B., Dehurib, S., Dashc, P.K., Pandad, G.: A reduced and comprehensible polynomial neural network for classification. Pattern Recognition 29(12), 1705–1712 (2008)
Daqi, G., Yan, J.: Classification methodologies of multilayer perceptions with sigmoid activation functions. Pattern Recognition 38(10), 1469–1482 (2005)
Chen, Y., Abraham, A., Yang, B.: Feature selection and classification using flexible neural tree. Neurocomputing 70(1-3), 305–313 (2006)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Chua, Y.S.: Efficient computations for large least square support vector machine classifiers. Pattern Recognition 24(1-3), 75–80 (2003)
Koknar-Tezel, S., Latecki, L.J.: Improving SVM classification on imbalanced time series data sets with ghost points. Knowledge and Information Systems 28(1), 1–23 (2011)
Benjamin, X.W., Japkowicz, N.: Boosting support vector machines for imbalanced data sets. Knowledge and Information Systems 25(1), 1–20 (2011)
Qinlan, J.R.: Introduction of decision trees. Machine Learning 1(1), 81–106 (1986)
Wang, L., Yang, B., Chen, Y., Abraham, A., Sun, H., Chen, Z., Wang, H.: Improvement of Neural Network Classifier using Floating Centroids. Knowledge and Information Systems (2011), doi: 10.1007/s10115-011-0410-8
Freund, Y.: Boosting a weak learning algorithm by majority. Information Computing 121(2), 256–285 (1995)
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. Applied Stat. 28(1), 100–108 (1979)
Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proc. of the Congress on Evolutionary Computation, pp. 81–86 (2001)
Huang, C.-P., Xiong, W.-L., Xu, B.-G.: Influnce of Inertia Weight on Astringency of Particle Swarm Algorithm and Its Improvement. Computer Engineering 34(12), 31–33 (2008)
Yang, B., Wang, L., Chen, Z., Chen, Y., Sun, R.: A novel classification method using the combination of FDPS and flexible neural tree. Neurocomputing 73(4-6), 690–699 (2010)
Blake, C.L., Merz, C.J., Newman, D.J., et al.: UCI Repository of Machine Learning Databases (2002), http://www.ics.uci.edu/mlearn/MLRepository.html
Chen, Y., Abraham, A.: Hybrid Neurocomputing for Detection of Breast Cancer. In: The Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST 2005), pp. 884–892 (2005)
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Zhang, L., Wang, L., Wang, X., Liu, K., Abraham, A. (2012). Research of Neural Network Classifier Based on FCM and PSO for Breast Cancer Classification. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_58
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DOI: https://doi.org/10.1007/978-3-642-28942-2_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28941-5
Online ISBN: 978-3-642-28942-2
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