臺灣博碩士論文加值系統

本研究以離散化隱性主題向量模型為基礎，提出一個新的特徵處理方式，透過特徵資料重組過濾機制，來提昇分類器的分類效能。同時也應用了近年來在隱性主題發掘(latent topic discovery)的演算方式，使其適當地應用於產生離散化的主題特徵向量。本研究將重點置於以特徵重組與隱性主題的抽取為基礎，再加上使用遺傳演算法來最佳化參數，研發出新的特徵前處理 (feature processing) 模型。

We demonstrate a feature processing procedure which emphasizes on the combination of original features with redundancy trimming steps. This procedure shows better classification result than traditional classification models. In our experiment, several key feature processing steps were proposed according to the type od the feature. These steps contains numerical to categorical feature value conversion, feature combination, feature redundancy discrimination, and latent structure discovery based on the concatenation of original features and extended feature set. The UCI machine learning repository is chosen as our demonstration to show the effect of our approach. In our prelimenary resullt, it shows that the classification accuracy outperforms the traditional naive bayes classifier while the ROC benchmark equals to the naïve bayes-only scenario. This result is believed to be a promising one on the feature processing procedure research.

1. 簡介
1.1 前言
1.2 研究動機
2. 國內外相關研究
2.1 分群方法
2.2 特徵重組
2.3 特徵維度縮減
2.4 隱性主題
2.5 參數最佳化
3. 研究方法
3.1 特徵分群離散化
3.2 特徵重組
3.3 特徵合併
3.4 隱性主題
3.5 參數最佳化
4. 實驗過程
5. 結論與未來討論
參考文獻

1. Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2009.
2.Jiawei Han, Micheline Kamber, and Jian Pei, "Data Mining: Concepts and Techniques", 3rd edition, Morgan Kaufmann, 2011.
3.S.B. Kotsiantis, "Supervised Machine Learning: A Review of Classification Techniques", Informatica 31(2007) 249-268, 2007.
4.Rish, Irina, "An empirical study of the naive Bayes classifier". IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001.
5.Neapolitan, R.E., Learning Bayesian Networks, Prentice Hall, Upper Saddle River, NJ, 2004.
6.Gurney, K., An Introduction to Neural Networks London: Routledge, 1997.
7.MacQueen, J. B., Some Methods for classification and Analysis of Multivariate Observations, in Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. pp. 281–297, 1967.
8.Agnar Aamodt and Enric Plaza, Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches, Artificial Intelligence Communications 7: 1, 39-52, 1994.
9.Eiben, A. E. et al , Genetic algorithms with multi-parent recombination, PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78–87, 1994.
10.Pawlak, Zdzisław, Rough sets. International Journal of Parallel Programming 11 (5): 341–356, 1982.
11.Von Altrock, Constantin, Fuzzy logic and NeuroFuzzy applications explained. Upper Saddle River, NJ: Prentice Hall PTR, 1995.
12.David A. Freedman, Statistical Models: Theory and Practice, Cambridge University Press, 2005.
13.Meilă, Marina, Comparing Clusterings by the Variation of Information. Learning Theory and Kernel Machines: 173–187, 2003.
14.Tutorial 12, Decision Trees Interactive Tutorial and Resources, online, http://www.cs.cmu.edu/~schneide/tut5/node42.html
15.Chun-Hsing Chen, Remote homology detection based on latent topic extraction in protein databases, January 2012
16.Pearson, K., On Lines and Planes of Closest Fit to Systems of Points in Space, Philosophical Magazine 2 (6): 559–572, 1901.
17.McLachlan, Discriminant Analysis and Statistical Pattern Recognition In: Wiley Interscience, 2004.
18.Gorsuch, R. L., Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum, 1983.
19.Borg, I., Groenen, P., Modern Multidimensional Scaling: theory and applications (2nd ed.). New York: Springer-Verlag. pp. 207–212, 2005.
20.J. B. Tenenbaum, V. de Silva and J. C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction", Science 290 (5500): 2319-2323, 22 December 2000.
21.Sam Roweis & Lawrence Saul, Nonlinear dimensionality reduction by locally linear embedding. Science, v.290 no.5500 , Dec.22, 2000. pp.2323--2326.
22.S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer and R. Harshman, Indexing by latent semantic analysis” Journal of the American Society for Information Science, vol. 41, no. 6, pp. 391-407, 1990.
23.T. Hofmann, “Unsupervised learning by probabilistic latent semantic analysis”, Machine Learning, vol. 42, no. 1, pp. 177–196, 2001.
24.D. M. Blei, A. Y. Ng and M. I. Jordan, “Latent Dirichlet allocation” Journal of Machine Learning Research, vol. 3, no. 5, pp. 993-1022, 2003.
25.T. Minka and J. Lafferty, “Expectation-propagation for the generative aspect model”, Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, pp. 352-359, 2002.
26.T. L. Griffiths and M. Steyvers, “Finding scientific topics”, Proceedings of the National Academy of Science, vol. 101, pp. 5228–5235, 2004
27.T. Minka, “Estimating a Dirichlet distribution”, Technical Report, MIT, 2000.
28.M. Girolami and A. Kaban, "On an equivalence between PLSI and LDA", Proceedings of the 26thAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 433-434, 2003
29.Abalone data set, in UCI machine learning repository, online, http://archive.ics.uci.edu/ml/datasets/Abalone
31.Image Segmentation data set, in UCI machine learning repository, online, http://archive.ics.uci.edu/ml/datasets/Image+Segmentation