跳到主要內容

臺灣博碩士論文加值系統

(35.172.136.29) 您好!臺灣時間:2021/07/26 20:24
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:李志文
研究生(外文):Chih-Wen Li
論文名稱:基於共同演化機制下區域性屬性加權的方法
論文名稱(外文):Local Feature Weighting Based on Coevolution Genetic Algorithm
指導教授:林志麟林志麟引用關係
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:32
中文關鍵詞:共同演化屬性加權基因演算法
外文關鍵詞:CoevolutionFeature weightingGenetic algorithm
相關次數:
  • 被引用被引用:1
  • 點閱點閱:160
  • 評分評分:
  • 下載下載:2
  • 收藏至我的研究室書目清單書目收藏:0
資料中過多不重要的屬性常會降低分類建模的準確度。針對此問題,過去研究常利用屬性加權(Feature weighting)的方式來改善。然而,由於資料的多樣化,對資料全面採取相同的屬性權重組合仍無法有效提升分類(Classification)效果。本研究以基因演算法(Genetic algorithms, GA)透過共同演化(Coevolution)的方式,產生多組不同的區域屬性權重組合,改善單一全域屬性權重組合無法有效提升分類效果的問題。在效能評估方面,實驗結果顯示,多個資料集使用本研究提出的多組區域屬性加權方法建立的分類器,皆會比使用單一屬性權重組合及未使用屬性加權的同類分類器有較好的分類準確度。
Redundant attributes often make building an effective classifier difficult. Feature weighting is one way to resolve this problem. However, as the diversity of the data increases, all data using the same set of feature weighting may be inappropriate. This work proposes a local feature weighting approach, which uses a coevolution genetic algorithm to generate multiple sets of local feature weighting. Our experimental results show that the accuracy of a classifier can be improved with multiple sets of local feature weighting than with a set of feature weighting and without using any feature weighting.
書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 2
第二章 相關研究探討 3
2.1 區域性屬性加權 3
2.2 共同演化 6
2.3 Adaptive-3FW 9
2.3.1 染色體編碼方式 9
2.3.2 Modified crossover 9
2.3.3 Modified mutation 11
2.4 K-means 12
2.5 KNN 13
第三章 研究方法 15
3.1 方法架構 15
3.2 CMFW 16
3.2.1 Create initial population 17
3.2.2 Evaluate global fitness 18
3.2.3 Local genetic operation 20
3.2.4 Evaluate local fitness 20
第四章 實驗 22
4.1 實驗設計 22
4.2 實驗資料集 23
4.3 實驗結果 24
第五章 結論 29
參考文獻 30
[1] Barros, A. C. A., & Cavalcanti, G. D. C. (2008). Combining global optimization algorithms with a simple adaptive distance for feature selection and weighting. Proceedings of the 2008 International Joint Conference on Neural Networks, 3518-3523.
[2] Beddoe, G. R., & Petrovic, S. (2006). Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering. European Journal of Operational Research, 175(2), 649-671.
[3] Chan, E. Y., Ching, W. K., Ng, M. K., & Huang, J. Z. (2004). An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recognition, 37(5), 943-952.
[4] Domeniconi, C., Gunopulos, D., Ma, S., Yan, B., Al-Razgan, M., & Papadopoulos, D. (2007). Locally adaptive metrics for clustering high dimensional data. Data Mining and Knowledge Discovery, 14(1), 63-97.
[5] Fernández, F., & Isasi, P. (2008). Local feature weighting in nearest prototype classification. IEEE Transactions on Neural Networks, 19(1), 40-53.
[6] Gançarski, P., & Blansché, A. (2008). Darwinian, lamarckian, and baldwinian (co) evolutionary approaches for feature weighting in K-means-based algorithms. IEEE Transactions on Evolutionary Computation, 12(5), 617-629.
[7] Hong, J. -H., & Cho, S. -B. (2006). Efficient huge-scale feature selection with speciated genetic algorithm. Pattern Recognition Letters, 27(2), 143-150.
[8] Kohavi, R., Langley, P., & Yun, Y. (1997). The utility of feature weighting in nearest-neighbor algorithms. Proceedings of ninth European conference on machine learning.
[9] Koumousis, V. K., & Katsaras, C. P. (2006). A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Transactions on Evolutionary Computation, 10(1), 19-28.
[10] Kudo, M., & Sklansky, J. (2000). Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1), 25-41.
[11] Lee, H., Kim, E., & Park, M. (2007). A genetic feature weighting scheme for pattern recognition. Integrated Computer-Aided Engineering, 14(2), 161-171.
[12] Oh, I. -S., Lee, J. -S., & Moon, B. -R. (2004). Hybrid genetic algorithms for feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11), 1424-1437.
[13] Paredes, R., & Vidal, E. (2000). A class-dependent weighted dissimilarity measure for nearest neighbor classification problems. Pattern Recognition Letters, 21(12), 1027-1036.
[14] Parsons, L., Haque, E., & Liu, H. (2004). Subspace clustering for high dimensional data: A review. SIGKDD Explorations, 6(1), 90-105.
[15] Potter, M. A., & De Jong, K. A. (2000). Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1), 1-29.
[16] Tahir, M. A., Bouridane, A., & Kurugollu, F. (2007). Simultaneous feature selection and feature weighting using hybrid tabu Search/K-nearest neighbor classifier. Pattern Recognition Letters, 28(4), 438-446.
[17] Tan, T., Fu, X., Zhang, Y., & Bourgeois, A. G. (2008). A genetic algorithm-based method for feature subset selection. Soft Computing, 12(2), 111-120.
[18] Vivencio, D. P., Hruschka Jr., E. R., Do Carmo Nicoletti, M., Dos Santos, E. B., & Galvão, S. D. C. O. (2007). Feature-weighted k-nearest neighbor classifier. Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, 481-485.
[19] Wang, X., Yang, J., Teng, X., Xia, W., & Jensen, R. (2007). Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters, 28(4), 459-471.
[20] Wettschereck, D., Aha, D. W., & Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11(1-5), 273-314.
[21] Yang, J., & Honavar, V. (1998). Feature subset selection using genetic algorithm. IEEE Intelligent Systems and their Applications, 13(2), 44-48.
[22] Özşen, S., & Güneş, S. (2009). Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems. Expert Systems with Applications, 36(1), 386-392.
[23] UCI Repository of Machine Learning Databases, Dept. Information and Computer Sciences, Univ. California, Irvine, 1998. Available online at http://www.ics.uci.edu/~mlearn/MLRepository.html
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top