政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125517

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125517

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政大典藏 > College of Commerce > Department of Statistics > Theses > Item 140.119/125517

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/125517

Title:	基於支持向量機計算的相互熵之特徵選取 A Feature Selection Study based on SVM and Mutual Entropy
Authors:	游上葦 Yu, Shang-Wei
Contributors:	周珮婷游上葦 Yu, Shang-Wei
Keywords:	機器學習特徵選取維度縮減支持向量機支援向量機熵相互熵 Machine learning Feature selection Dimension reduction Support Vector Machine SVM Entropy Shannon Entropy Mutual Entropy
Date:	2019
Issue Date:	2019-09-05 15:42:06 (UTC+8)
Abstract:	特徵選取為機器學習領域中一重要部分，適當的選取特徵(變數)，除了減少機器運算時間、人力與金錢外，也可以避免模型過度配適或是欠擬和的情況發生。雖然經過多年發展已有很多特徵選取的方法，但同一種模型，不一定適用所有資料情況，因此提出新方法希望在特徵選取上會有更多選擇。　　本文提出一新方法衡量變數之間的關係，使用資訊理論中熵的概念，結合分類器支持向量機，獲取變數間關係，並將變數分群，給予每群適當的相對應分數，以此篩選變數。本文採用半監督式學習，計算敏感度、特異度與準確度之平均及所使用變數數量，並以高斯混合模型搭配EM演算法利用KS檢定之檢定統計量定義資料重要變數，評估方法能否選取重要變數，本文一共使用2筆模擬資料與5筆真實資料，並將結果與各大方法比較，結果顯示在各資料集中皆有穩定表現，即使在變數少的情況下也能有不錯表現。 Feature selection technique plays a significant role in machine learning. Selecting features (variables) adequately can not only reduce the expenditure, operating time in machine and the cost of labor but also prevent under fitting or overfitting. Although lots of feature selection methods have been developed for decades, it is impossible to apply a unique method to all types of data sets. In this study, we propose a new method to calculate the correlation between variables based on the Shannon entropy from information theory and SVM classifier. Variables are grouped into several clusters and selected by the new correlation measurement. Besides, we define the importance of variable by the test statistic of KS test using Gaussian mixed model and E-M algorithm for the propose of result assessment. The performance of proposed method on two simulated data and five real data are demonstrated and compared with other feature selection methods. The predicted results are stable through the proposed method with a reduced dataset.
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.
Description:	碩士國立政治大學統計學系 106354024
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0106354024
Data Type:	thesis
DOI:	10.6814/NCCU201900996
Appears in Collections:	[Department of Statistics] Theses

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