Abstract
When the traditional decision tree algorithm is applied to the field of network security analysis, due to the unreasonable property selection method, the overfitting problem may be caused, and the accuracy of the constructed decision tree is low. Therefore, this paper proposes a decision tree selection method based on improved manifold learning algorithm. The manifold learning algorithm maps the high-dimensional feature space to the low-dimensional space, so the algorithm can acquire the essential attributes of the data source. According to this, the problems of low accuracy and overfitting can be solved. Aiming at the traditional manifold learning algorithms are sensitive to noise and the algorithms converges slowly, this paper proposes a Global and Local Mapping manifold learning algorithm, and this method is used to construct a decision tree. The experimental results show that compared with the traditional ID3 decision tree construction algorithm, the improved method reduces 2.16% and 1.626% in false positive rate and false negative rate respectively.
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References
Kim, S.Y., Upneja, A.: Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Econ. Model. 36(1), 354–362 (2014)
Pai, P.F., Changliao, L.H., Lin, K.P.: Analyzing basketball games by a support vector machines with decision tree model. Neural Comput. Appl. 232, 1–9 (2016)
Azad, M., Moshkov, M.: Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions. Eur. J. Oper. Res. 263, 910–921 (2017)
Ai, X., Wu, J., Cui, Z.: Broaden the minority class space for decision tree induction using antigen-derived detectors. Knowl.-Based Syst. 137, 196–205 (2017)
Cicalese, F., Laber, E., Saettler, A.: Decision trees for function evaluation: simultaneous optimization of worst and expected cost. Algorithmica 79, 1–34 (2013)
Zhang, Q., Zhang, Q., Zhang, L.: Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recogn. 48(10), 3102–3112 (2015)
Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Guo, F., Chao, L., Wang, H. (2019). A Decision Tree Candidate Property Selection Method Based on Improved Manifold Learning Algorithm. In: Zheng, J., Xiang, W., Lorenz, P., Mao, S., Yan, F. (eds) Ad Hoc Networks. ADHOCNETS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-05888-3_24
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DOI: https://doi.org/10.1007/978-3-030-05888-3_24
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