Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/124685| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 周珮婷 | zh_TW |
| dc.contributor.advisor | CHOU, PEI-TING | en_US |
| dc.contributor.author | 董承 | zh_TW |
| dc.contributor.author | Tung, Chen | en_US |
| dc.creator | 董承 | zh_TW |
| dc.creator | Tung, Chen | en_US |
| dc.date | 2019 | en_US |
| dc.date.accessioned | 2019-08-07T08:01:51Z | - |
| dc.date.available | 2019-08-07T08:01:51Z | - |
| dc.date.issued | 2019-08-07T08:01:51Z | - |
| dc.identifier | G0106354014 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/124685 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 統計學系 | zh_TW |
| dc.description | 106354014 | zh_TW |
| dc.description.abstract | 不平衡資料在各個領域中是一種常見的資料型態，少數類別通常是主要研究的目標，例如：異常偵測、風險管控、醫療診斷等領域。基因微陣列資料是利用生物晶片提取基因表現情形將其數據化，並對其進行研究分析，而此資料之特色為樣本數少卻有非常高的維度。本研究基於以上兩者之問題，對高維不平衡之基因微陣列資料，以雙分群方法之概念做變數選取，並且與F-test method、Cho’s method以及使用全部變數做比較，研究結果顯示本研究方法與F-test method表現接近且優於Cho’s method和使用全部變數。 | zh_TW |
| dc.description.abstract | Imbalanced data is a common data type in different fields, for example, novelty detection, risk management, medical diagnosis and so on. In these data types, minority class is usually the main target to study. In this study, we focus on microarray data. Microarray data is obtained by using biochips to extract gene expression, and then analyze it. The characteristics of this data is that the sample size is small but with a very high dimension. Based on the problems above, this study selects features of high-dimensional imbalanced microarray data by the concept of biclustering algorithm, and compares it with the F-test method, the Cho`s method, and using all variables. The performance of proposed method is similar to the F-test method and superior to the Cho`s method and using all variables. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1\n第二章 文獻探討 3\n第一節 不平衡資料之分類問題 3\n第二節 基因微陣列簡介 5\n第三節 基因微陣列資料之變數選取 6\n第四節 雙分群方法 7\n第三章 研究方法與過程 9\n第一節 所使用之演算法 9\n第二節 分類評估指標 11\n第三節 研究方法 14\n第四節 F-test & Cho’s Method 15\n第四章 研究結果與分析 17\n第一節 基因微陣列資料 17\n第二節 模擬資料 18\n第三節 基因微陣列資料研究結果 19\n第四節 模擬資料研究結果 26\n第五章 結論與建議 31\n第一節 結論 31\n第二節 未來研究方向與建議 32\n參考文獻 33\n附錄 37 | zh_TW |
| dc.format.extent | 1944743 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0106354014 | en_US |
| dc.subject | 不平衡資料 | zh_TW |
| dc.subject | 高維度資料 | zh_TW |
| dc.subject | 基因微陣列資料 | zh_TW |
| dc.subject | 雙分群方法 | zh_TW |
| dc.subject | 變數選取 | zh_TW |
| dc.subject | Imbalanced data | en_US |
| dc.subject | High-dimensional data | en_US |
| dc.subject | Microarray data | en_US |
| dc.subject | Biclustering algorithm | en_US |
| dc.subject | Feature selection | en_US |
| dc.title | 高維不平衡基因資料的變數選取 | zh_TW |
| dc.title | Feature selection for high-dimensional imbalanced microarray data | en_US |
| dc.type | thesis | en_US |
| dc.relation.reference | Akbani, R., Kwek, S., & Japkowicz, N. (2004). Applying support vector machines to imbalanced datasets. Paper presented at the European conference on machine learning.\nAlon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., & Levine, A. J. J. P. o. t. N. A. o. S. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. 96(12), 6745-6750.\nBellman, R. J. S. (1966). Dynamic programming. 153(3731), 34-37.\nBlum, A. L., & Langley, P. J. A. i. (1997). Selection of relevant features and examples in machine learning. 97(1-2), 245-271.\nChawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. J. J. o. a. i. r. (2002). SMOTE: synthetic minority over-sampling technique. 16, 321-357.\nChen, J.-X., Cheng, T.-H., Chan, A. L., & Wang, H.-Y. (2004). An application of classification analysis for skewed class distribution in therapeutic drug monitoring-the case of vancomycin. Paper presented at the 2004 IDEAS Workshop on Medical Information Systems: The Digital Hospital (IDEAS-DH`04).\nCho, J.-H., Lee, D., Park, J. H., & Lee, I.-B. J. F. l. (2003). New gene selection method for classification of cancer subtypes considering within‐class variation. 551(1-3), 3-7.\nCohen, G., Hilario, M., Sax, H., & Hugonnet, S. (2003). Data imbalance in surveillance of nosocomial infections. Paper presented at the International Symposium on Medical Data Analysis.\nCortes, C., & Vapnik, V. J. M. l. (1995). Support-vector networks. 20(3), 273-297.\nDas, S. (2001). Filters, wrappers and a boosting-based hybrid for feature selection. Paper presented at the Icml.\nDel Castillo, M. D., & Serrano, J. I. J. A. S. E. N. (2004). A multistrategy approach for digital text categorization from imbalanced documents. 6(1), 70-79.\nDing, C., Peng, H. J. J. o. b., & biology, c. (2005). Minimum redundancy feature selection from microarray gene expression data. 3(02), 185-205.\nDudoit, S., Fridlyand, J., & Speed, T. P. J. J. o. t. A. s. a. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. 97(457), 77-87.\nFodor, S. P., Read, J. L., Pirrung, M. C., Stryer, L., Lu, A. T., & Solas, D. J. s. (1991). Light-directed, spatially addressable parallel chemical synthesis. 251(4995), 767-773.\nFreund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. Paper presented at the icml.\nGolub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., . . . Caligiuri, M. A. J. s. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. 286(5439), 531-537.\nGordon, G. J., Jensen, R. V., Hsiao, L.-L., Gullans, S. R., Blumenstock, J. E., Ramaswamy, S., . . . Bueno, R. J. C. r. (2002). Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. 62(17), 4963-4967.\nGravier, E., Pierron, G., Vincent‐Salomon, A., Gruel, N., Raynal, V., Savignoni, A., . . . Cancer. (2010). A prognostic DNA signature for T1T2 node‐negative breast cancer patients. 49(12), 1125-1134.\nHartigan, J. A. J. J. o. t. a. s. a. (1972). Direct clustering of a data matrix. 67(337), 123-129.\nHe, H., Garcia, E. A. J. I. T. o. K., & Engineering, D. (2008). Learning from imbalanced data. (9), 1263-1284.\nHira, Z. M., & Gillies, D. F. J. A. i. b. (2015). A review of feature selection and feature extraction methods applied on microarray data. 2015.\nHong, X., Chen, S., & Harris, C. J. J. I. T. o. n. n. (2007). A kernel-based two-class classifier for imbalanced data sets. 18(1), 28-41.\nJapkowicz, N., & Stephen, S. J. I. d. a. (2002). The class imbalance problem: A systematic study. 6(5), 429-449.\nJapkowicz, N. J. M. L. (2001). Supervised versus unsupervised binary-learning by feedforward neural networks. 42(1-2), 97-122.\nKotsiantis, S., Kanellopoulos, D., Pintelas, P. J. G. I. T. o. C. S., & Engineering. (2006). Handling imbalanced datasets: A review. 30(1), 25-36.\nKubat, M., Holte, R. C., & Matwin, S. J. M. l. (1998). Machine learning for the detection of oil spills in satellite radar images. 30(2-3), 195-215.\nLiu, X.-Y., Wu, J., Zhou, Z.-H. J. I. T. o. S., Man,, & Cybernetics, P. B. (2008). Exploratory undersampling for class-imbalance learning. 39(2), 539-550.\nLiu, X.-Y., & Zhou, Z.-H. (2006). The influence of class imbalance on cost-sensitive learning: An empirical study. Paper presented at the Sixth International Conference on Data Mining (ICDM`06).\nLusa, L. J. B. b. (2010). Class prediction for high-dimensional class-imbalanced data. 11(1), 523.\nMani, I., & Zhang, I. (2003). kNN approach to unbalanced data distributions: a case study involving information extraction. Paper presented at the Proceedings of workshop on learning from imbalanced datasets.\nMcCarthy, K., Zabar, B., & Weiss, G. (2005). Does cost-sensitive learning beat sampling for classifying rare classes? Paper presented at the Proceedings of the 1st international workshop on Utility-based data mining.\nPérez, J. M., Muguerza, J., Arbelaitz, O., Gurrutxaga, I., & Martín, J. I. (2005). Consolidated tree classifier learning in a car insurance fraud detection domain with class imbalance. Paper presented at the International Conference on Pattern Recognition and Image Analysis.\nPhua, C., Alahakoon, D., & Lee, V. J. A. s. e. n. (2004). Minority report in fraud detection: classification of skewed data. 6(1), 50-59.\nPudil, P., Novovičová, J., & Kittler, J. J. P. r. l. (1994). Floating search methods in feature selection. 15(11), 1119-1125.\nRadivojac, P., Korad, U., Sivalingam, K. M., & Obradovic, Z. (2003). Learning from class-imbalanced data in wireless sensor networks. Paper presented at the 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No. 03CH37484).\nRamey, J. (2016). Datamicroarray: collection of data sets for classification. In: URL https://github. com/boost-R/datamicroarray.\nRaskutti, B., & Kowalczyk, A. J. A. S. E. N. (2004). Extreme re-balancing for SVMs: a case study. 6(1), 60-69.\nSaeys, Y., Inza, I., & Larrañaga, P. J. b. (2007). A review of feature selection techniques in bioinformatics. 23(19), 2507-2517.\nShipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C., . . . Pinkus, G. S. J. N. m. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. 8(1), 68.\nSingh, D., Febbo, P. G., Ross, K., Jackson, D. G., Manola, J., Ladd, C., . . . Richie, J. P. J. C. c. (2002). Gene expression correlates of clinical prostate cancer behavior. 1(2), 203-209.\nSun, Y., Kamel, M. S., Wong, A. K., & Wang, Y. J. P. R. (2007). Cost-sensitive boosting for classification of imbalanced data. 40(12), 3358-3378.\nWest, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., . . . Nevins, J. R. J. P. o. t. N. A. o. S. (2001). Predicting the clinical status of human breast cancer by using gene expression profiles. 98(20), 11462-11467.\nYang, K., Cai, Z., Li, J., & Lin, G. (2006). A stable gene selection in microarray data analysis. BMC bioinformatics, 7(1), 228.\nYoon, K., & Kwek, S. (2005). An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics. Paper presented at the Fifth International Conference on Hybrid Intelligent Systems (HIS`05).\nYuan, J., Li, J., & Zhang, B. (2006). Learning concepts from large scale imbalanced data sets using support cluster machines. Paper presented at the Proceedings of the 14th ACM international conference on Multimedia.\nZheng, Z., Wu, X., & Srihari, R. J. A. S. E. N. (2004). Feature selection for text categorization on imbalanced data. 6(1), 80-89.\nZhou, Z.-H., Liu, X.-Y. J. I. T. o. K., & Engineering, D. (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. (1), 63-77.\nZou, K. H., O’Malley, A. J., & Mauri, L. J. C. (2007). Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. 115(5), 654-657. | zh_TW |
| dc.identifier.doi | 10.6814/NCCU201900460 | en_US |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.openairetype | thesis | - |
| item.grantfulltext | open | - |
| item.cerifentitytype | Publications | - |
| item.fulltext | With Fulltext | - |
| Appears in Collections: | 學位論文 | |
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| 401401.pdf | 1.9 MB | Adobe PDF2 | View/Open |
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