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Yue T, He Z, Li C, Hu Z, Li Y. Lightweight fine-grained classification for scientific paper. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model.
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Affiliation(s)
- Tan Yue
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Zihang He
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Chang Li
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
| | - Zonghai Hu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yong Li
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
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Zhang K, Liu X, Jiang J, Li W, Wang S, Liu L, Zhou X, Wang L. Prediction of postoperative complications of pediatric cataract patients using data mining. J Transl Med 2019; 17:2. [PMID: 30602368 PMCID: PMC6317183 DOI: 10.1186/s12967-018-1758-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/21/2018] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown. METHODS Apriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications. RESULTS Average classification accuracies in three binary classification problems are over 75%. Second, the relationship between the classification performance and the number of random forest tree is studied. Results show except for gender and age at surgery (AS); other attributes are related to complications. Except for the secondary IOL placement, operation mode, AS and area of cataracts; other attributes are related to SLPVA. Except for the gender, operation mode, and laterality; other attributes are related to the AHIP. Next, the association rules related to the complications are mined out. Then additional 50 data were used to test the performance of RF and NB, both of then obtained the accuracies of over 65% for three classification problems. Finally, we developed a webserver to assist doctors. CONCLUSIONS The postoperative complications of pediatric cataracts patients can be predicted. Then the factors related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors.
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Affiliation(s)
- Kai Zhang
- School of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China.,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Xiyang Liu
- School of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China. .,Institute of Software Engineering, Xidian University, Xi'an, 710071, China. .,School of Software, Xidian University, Xi'an, 710071, China.
| | - Jiewei Jiang
- School of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China.,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Wangting Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Shuai Wang
- School of Software, Xidian University, Xi'an, 710071, China
| | - Lin Liu
- School of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China
| | - Xiaojing Zhou
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Liming Wang
- School of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China.,Institute of Software Engineering, Xidian University, Xi'an, 710071, China.,School of Software, Xidian University, Xi'an, 710071, China
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Ibáñez A, Armañanzas R, Bielza C, Larrañaga P. Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices. J Assoc Inf Sci Technol 2016. [DOI: 10.1002/asi.23467] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Alfonso Ibáñez
- Computational Intelligence Group; Departamento de Inteligencia Artificial; Universidad Politécnica de Madrid; Campus de Montegancedo s/n Boadilla del Monte 28660 Spain
| | - Rubén Armañanzas
- Krasnow Institute for Advanced Study; George Mason University; 4400 University Drive Fairfax VA 22030
| | - Concha Bielza
- Computational Intelligence Group; Departamento de Inteligencia Artificial; Universidad Politécnica de Madrid; Campus de Montegancedo s/n Boadilla del Monte 28660 Spain
| | - Pedro Larrañaga
- Computational Intelligence Group; Departamento de Inteligencia Artificial; Universidad Politécnica de Madrid; Campus de Montegancedo s/n Boadilla del Monte 28660 Spain
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