Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/135930
|
| Title: | 基於類別異質性結構的監督式學習
Supervised Learning with Potential Label Heterogeneity |
| Authors: | 黃俊嘉
Huang, Jiun-Jia |
| Contributors: | 周珮婷
黃俊嘉
Huang, Jiun-Jia |
| Keywords: | 潛在類別
異質性
分類預測
Potential categories
Heterogeneity
Classification prediction |
| Date: | 2021 |
| Issue Date: | 2021-07-01 17:30:54 (UTC+8) |
| Abstract: | 在過往的研究中，研究人員常常將研究重心放在找出資料的潛在類別，並透過資料的異質性結構定義出新類別，於是本研究提出基於同類別的異質性結構將各個原始類別拆成數個子類別以提高分類預測準確率。本研究以建立標籤內嵌樹的方法進行分類預測，此分類預測方法是先計算類別支配矩陣後，藉由此矩陣進行階層式分群，並由偽概似機率進行分類預測，而本研究比較使用此方法和常見分類器的分類預測表現差異，也比較常見分類器在使用子類別及原始類別的分類預測差異。研究結果顯示所提出的子類別方法，在異質性資料確實會擁有較高的預測正確率。另外，本研究發現在多數的分類器，以子類別預測能提升分類表現，但是需要考慮資料本身是否含有異質性結構。
In previous studies, researchers often focused their research on identifying potential categories of data and defining new categories through the heterogeneous structure of the data. Therefore, in this study, the original categories were divided into sub-categories based on the heterogeneous structure of the same category. Each sub-category is then classified and predicted by the method used in this study. This classification prediction method calculates the label dominance matrix, uses the matrix to group hierarchically, and uses the pseudo-likelihood probability to perform classification prediction. This research will compare the prediction accuracy rates of the common classifiers that use the original categories for classification prediction and this proposed method that uses the subcategories. The research results show that this proposed method will indeed have better results in some datasets. In addition, this study also compared whether the classification prediction using the split into sub-categories and the classification prediction using the original category will increase the accuracy of the prediction of various classifiers. It turns out that most of the classifiers have an improvement. Nevertheless, we need to consider if a heterogeneous structure exists in a category first before applying the proposed method. |
| Reference: | 王郁琮. (2014). 台灣青少年異質性憂鬱發展軌跡之性別差異及與違常行為之關係. 中華心理衛生學刊, 27(1), 97-130.
尹霞雲、朱翠英、黎志華、蔡泰生. (2014). 留守兒童情緒和行為問題特徵的潛在類別分析：基於個體為中心的研究視角. 心理科學, 37(2), 329-334.
陳杏佳. (2005). 精神分裂症住院病患攻擊行為之異質性與危險因子研究. 臺灣大學護理學研究所學位論文, 1-181.
Allwein, E. L., Schapire, R. E., & Singer, Y. (2000). Reducing multiclass to binary: Aunifying approach for margin classifiers. Journal of machine learning research, 1(Dec), 113-141.
Allsopp, K., Read, J., Corcoran, R., & Kinderman, P. (2019). Heterogeneity in psychiatric diagnostic classification. Psychiatry research, 279, 15-22.
Blanco-Calvo, M., Concha, Á., Figueroa, A., Garrido, F., & Valladares-Ayerbes, M. (2015). Colorectal cancer classification and cell heterogeneity: a systems oncology approach. International journal of molecular sciences, 16(6), 13610-13632.
Caliński, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1), 1-27.
Dahl, K. H., Simonsen, G. S., Olsvik, Ø., & Sundsfjord, A. (1999). Heterogeneity in
the vanB gene cluster of genomically diverse clinical strains of vancomycin-resistant enterococci. Antimicrobial agents and chemotherapy, 43(5), 1105-1110.
Farmer, A. E., McGuffin, P., & Spitznagel, E. L. (1983). Heterogeneity in schizophrenia: a cluster-analytic approach. Psychiatry Research, 8(1), 1-12.
Fitzpatrick, A. M., Teague, W. G., Meyers, D. A., Peters, S. P., Li, X., Li, H., ... & National Institutes of Health. (2011). Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program. Journal of allergy and clinical immunology, 127(2), 382-389.
Hastie, T., & Tibshirani, R. (1998). Classification by pairwise coupling. Paper presented at the Advances in neural information processing systems.
Hsieh, F., & Chou, E. P. (2021). Categorical Exploratory Data Analysis: From Multiclass Classification and Response Manifold Analytics Perspectives of Baseball Pitching Dynamics. Entropy, 23(7), 792.
Krzanowski, W. J., & Lai, Y. T. (1988). A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 23-34.
Krijthe, J.H. & Loog, M. (2015). Implicitly Constrained Semi-Supervised Least Squares Classification. In E. Fromont, T. de Bie, & M. van Leeuwen, eds. 14th International Symposium on Advances in Intelligent Data Analysis XIV (Lecture Notes in Computer Science Volume 9385). Saint Etienne. France, pp. 158-169.
Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14(2), 157-168.
Leisch F, Dimitriadou E (2021). mlbench: Machine Learning Benchmark Problems. R package version 2.1-3.
MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).
Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159-179.
Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of machine learning research, 5(Jan), 101-141.
Reser, M. P., Allott, K. A., Killackey, E., Farhall, J., & Cotton, S. M. (2015). Exploring cognitive heterogeneity in first-episode psychosis: What cluster analysis can reveal. Psychiatry Research, 229(3), 819-827.
Shaw, S. Y., Shah, L., Jolly, A. M., & Wylie, J. L. (2008). Identifying heterogeneity among injection drug users: a cluster analysis approach. American Journal of Public Health, 98(8), 1430-1437.
Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236-244.
Wanberg, C. R., & Marchese, M. C. (1994). Heterogeneity in the Unemployment Experience: A Cluster Analytic Investigation 1. Journal of Applied Social Psychology, 24(6), 473-488. |
| Description: | 碩士
國立政治大學
統計學系
108354013 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108354013 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100555 |
| Appears in Collections: | [統計學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 401301.pdf | | 1934Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
