簡易檢索 / 詳目顯示

研究生: 戴士傑
Shih-Chieh Tai
論文名稱: 非凌駕式排序基因演算法演化結果之分析
Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification
指導教授: 楊朝龍
Chao-Lung Yang
口試委員: 歐陽超
Chao Ou-Yang
鄭辰仰
Chen-Yang Cheng
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 49
中文關鍵詞: 資料分群NSGAII-SCC資料分類逐步迴歸相似度矩陣
外文關鍵詞: Clustering, Classification, NSGAII-SCC, Stepwise Regression, Similarity Matrix
相關次數: 點閱:433下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

本研究的目標在發展一個資料分析方法來解析多準則決策模型(Non Dominated Sorting Genetic Algorithm – Sequential Clustering Classification, NSGAII-SCC) 所產生之分群及分類的結果,以提供該模型使用者較佳之欄位選取(feature selection)。NSGAII-SCC針對不同的資料集:效能資料(Q資料)及與效能相關資料(X資料)來進行分群以及分類,其目的在於選擇適當的欄位以同時維持資料分群的密集性(compactness)及資料分類的預測準確度(accuracy)之品質。然而先前的研究多著重於選取最佳分群及分類結果作為資料分析時的參考,本研究則著重於進一步分析基因演算最佳化過程中所產生之演化資訊。透過逐步迴歸方法消除多餘欄位,並使用資料分群來探索欄位選擇對分群及分類結果之影響。實驗結果發現,本研究所提出之方法能進一步將NSGAII-SCC模型的運算結果進行更精確的解析,以提升NSGAII-SCC在資料分析的能力。


This research aims to develop a data analysis framework to analyze the data generated by performing clustering and classification sequentially. The clustering and classification procedures are conducted on two different types of data, respectively: one contains the performance measure dataset denoted as Q dataset; the other contains the factors or relevant information regarding the clustering result, denoted as X dataset. Non Dominated Sorting Genetic Algorithm – Sequential Clustering Classification (NSGAII-SCC) with stepwise regression was proposed to investigate the multiple solutions generated from the proposed NSGA which aims to optimize the compactness of clustering and accuracy of classification. In this research, the stepwise regression model and frequency-based similarity matrix were applied to eliminate the redundant features in the datasets and identify the significant features for classification, respectively. The experiment result shows that the proposed methods are able to identify the redundant features and also provide the useful information for the evaluation of clustering and classification results.

ABSTRACT 摘要 致謝 CONTENTS LIST OF FIGURES LIST OF TABLES CHAPTER 1 INTRODUCTION 1.1 BACKGROUND 1.2 RESEARCH OBJECTIVE 1.3 ORGANIZATION CHAPTER 2 LITERATURE REVIEW 2.1 CLUSTERING AND CLASSIFICATION 2.2 MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM (MOEA) 2.3 NON-DOMINATED SORTING GENETIC ALGORITHM CHAPTER 3 METHODOLOGY 3.1 RESEARCH FRAMEWORK 3.2 STAGE 1: IDENTIFY DATA 3.3 STAGE 2: NON-DOMINATED SORTING GENETIC ALGORITHM 3.3.1 Chromosome Representation 3.3.2 Sequential clustering and classification 3.3.3 Fitness function for multi-objective optimization 3.3.4 Non-Dominated Sorting 3.4 STAGE 3: RECORD ANALYSIS 3.4.1 Stepwise Regression 3.4.2 Feature clustering 3.4.3 Frequency based similarity matrix CHAPTER 4 EXPERIMENTAL RESULT 4.1 DATASET AND PARAMETER SETTING 4.2 EXPERIMENT RESULT 4.2.1 US energy data 4.2.2 IMDB movie data 4.2.3 IBM human resource data CHAPTER 5 DISCUSSION AND CONCLUSION REFERENCE APPENDIX

Aggarwal, C. C. (2014). Data Classification: Algorithms and Applications: Chapman and Hall/CRC.
Aggarwal, C. C., et al. (2013). Data clustering: algorithms and applications: Chapman and Hall/CRC.
Bandyopadhyay, S., et al. (2013). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing, 24(4), 707-716.
Bandyopadhyay, S., et al. (2002). Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition, 35(6), 1197-1208.
Cai, W., et al. (2009). A simultaneous learning framework for clustering and classification. Pattern Recognition, 42(7), 1248-1259. doi:https://doi.org/10.1016/j.patcog.2008.11.029
Coello, C. A. C., et al. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5): Springer.
Deb, K., et al. (2000). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. Paper presented at the Parallel Problem Solving from Nature PPSN VI, Berlin, Heidelberg. http://dx.doi.org/10.1007/3-540-45356-3_83
Deb, K., et al. (2016). Multi-objective optimization Decision Sciences: Theory and Practice (pp. 145-184): CRC Press.
Feng, D., et al. (2008). Application study of data mining on customer relationship management in E-commerce. Paper presented at the Computer-Aided Industrial Design and Conceptual Design, 2008. CAID/CD 2008. 9th International Conference on.
Goldberg, D. E., et al. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Grubesic, T. H. (2006). On The Application of Fuzzy Clustering for Crime Hot Spot Detection. Journal of Quantitative Criminology, 22(1), 77. doi:10.1007/s10940-005-9003-6
Han, J., et al. (2011). Data Mining: Concepts and Techniques: Elsevier Science.
Hillard, D., et al. (2008). Computer-assisted topic classification for mixed-methods social science research. Journal of Information Technology & Politics, 4(4), 31-46.
Hosseini, S. M. S., et al. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666.
Jain, A. K., et al. (1988). Algorithms for Clustering Data (pp. 1-6).
Jain, A. K., et al. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Jin, X., et al. (2010). Partitional Clustering. In C. Sammut, et al. (Eds.), Encyclopedia of Machine Learning (pp. 766-766). Boston, MA: Springer US.
Kesavaraj, G., et al. (2013, 4-6 July 2013). A study on classification techniques in data mining. Paper presented at the 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).
Knowles, J. D., et al. (2001). Reducing local optima in single-objective problems by multi-objectivization. Paper presented at the International Conference on Evolutionary Multi-Criterion Optimization.
Kodali, S. P., et al. (2008). Multi-objective optimization of surface grinding process using NSGA II. Paper presented at the Emerging Trends in Engineering and Technology, 2008. ICETET'08. First International Conference on.
Kondayya, D., et al. (2011). An integrated evolutionary approach for modelling and optimization of wire electrical discharge machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(4), 549-567.
Mandal, D., et al. (2007). Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. Journal of Materials Processing Technology, 186(1–3), 154-162.
Manning, C. D., et al. (2008). Introduction to information retrieval (Vol. 1): Cambridge university press Cambridge.
Michalewicz, Z., et al. (2004). How to Solve It: Modern Heuristics: Springer.
Mitra, K., et al. (2004). Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm. Chemical Engineering Science, 59(2), 385-396.
Nikam, S. S. (2015). A comparative study of classification techniques in data mining algorithms. Orient J Comput Sci Technol, 8(1), 13-19.
Quyen, N. T. P. (2016). Data Analysis Framework of Constrained Clustering and Sequential Clustering Classification. (Doctor of Philosophy Dissertation), National Taiwan University of Science and Technology, Taipei, Taiwan.
Rokach, L., et al. (2005). Clustering Methods. In O. Maimon, et al. (Eds.), Data Mining and Knowledge Discovery Handbook (pp. 321-352). Boston, MA: Springer US.
Romesburg, C. (2004). Cluster analysis for researchers: Lulu. com.
Rudolph, G. (2001). Evolutionary search under partially ordered fitness sets. Retrieved from
Tan, P.-N., et al. (2005). Introduction to Data Mining, (First Edition): Addison-Wesley Longman Publishing Co., Inc.
Vapnik, V. (2013). The nature of statistical learning theory: Springer science & business media.
Weinhardt, C., et al. (2009). Cloud Computing – A Classification, Business Models, and Research Directions. Business & Information Systems Engineering, 1(5), 391-399.
Wu, Z., et al. (1993). An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1101-1113. doi:10.1109/34.244673
Yusoff, Y., et al. (2011). Overview of NSGA-II for Optimizing Machining Process Parameters. Procedia Engineering, 15, 3978-3983.
Zitzler, E., et al. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation, 8(2), 173-195.

無法下載圖示 全文公開日期 2023/02/10 (校內網路)
全文公開日期 本全文未授權公開 (校外網路)
全文公開日期 本全文未授權公開 (國家圖書館:臺灣博碩士論文系統)
QR CODE