scholarly journals Research on an Identification Method for Gas Disaster Risk Based on the Selective Ensemble Classification Model

ACS Omega ◽  
2021 ◽  
Author(s):  
Rong Liang ◽  
Qiaolin Lv ◽  
Pengtao Jia ◽  
Zhilong Zhao ◽  
Chengyixiong Xu
Keyword(s):  
Disaster Risk ◽  
Ensemble Classification ◽  
Classification Model ◽  
Identification Method ◽  
Selective Ensemble ◽  
Gas Disaster

scholarly journals A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Ensemble Classification ◽  
Classification Method ◽  
Ultrasound Images ◽  
Selective Ensemble ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

Selective Ensemble Classification of Image Steganalysis Via Deep Q Network

2019 ◽  
Vol 26 (7) ◽  
pp. 1065-1069 ◽  
Author(s):  
Danni Ni ◽  
Guorui Feng ◽  
Liquan Shen ◽  
Xinpeng Zhang
Keyword(s):  
Ensemble Classification ◽  
Image Steganalysis ◽  
Selective Ensemble

scholarly journals (Un)Trendy Japan: Twitter bots and the 2017 Japanese general election

2019 ◽  
Vol 15 (3) ◽  
pp. 497-514
Author(s):  
Jozef Michal Mintal ◽  
Róbert Vancel
Keyword(s):  
User Profile ◽  
General Election ◽  
Ensemble Classification ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Political Actors ◽  
Political Processes ◽  
Political Events ◽  
The Uk ◽  
The Impact

AbstractSocial networking services (SNSs) can significantly impact public life during important political events. Thus, it comes as no surprise that different political actors try to exploit these online platforms for their benefit. Bots constitute a popular tool on SNSs that appears to be able to shape public opinion and disrupt political processes. However, the role of bots during political events in a non-Western context remains largely under-studied. This article addresses the question of the involvement of Twitter bots during electoral campaigns in Japan. In our study, we collected Twitter data over a fourteen-day period in October 2017 using a set of hashtags related to the 2017 Japanese general election. Our dataset includes 905,215 tweets, 665,400 of which were unique tweets. Using a supervised machine learning approach, we first built a custom ensemble classification model for bot detection based on user profile features, with an area under curve (AUC) for the test set of 0.998. Second, in applying our model, we estimate that the impact of Twitter bots in Japan was minor overall. In comparison with similar studies conducted during elections in the US and the UK, the deployment of Twitter bots involved in the 2017 Japanese general election seems to be significantly lower. Finally, given our results on the level of bots on Twitter during the 2017 Japanese general election, we provide various possible explanations for their underuse within a broader socio-political context.

Concept Attribute Labeling and Context-Aware Named Entity Recognition in Electronic Health Records

2018 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Alexandra Pomares-Quimbaya ◽  
Rafael A. Gonzalez ◽  
Oscar Mauricio Muñoz Velandia ◽  
Angel Alberto Garcia Peña ◽  
Julián Camilo Daza Rodríguez ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Ad Hoc ◽  
Named Entity Recognition ◽  
Ensemble Classification ◽  
Entity Recognition ◽  
Classification Model ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health ◽  
Concept Attribute

Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.

Study of Financial Warning Ensemble Model for Listed Companies Based on Unbalanced Classification Perspective

2020 ◽  
Vol 16 (1) ◽  
pp. 32-48
Author(s):  
Wei Cong
Keyword(s):  
Decision Tree ◽  
Listed Companies ◽  
Hellinger Distance ◽  
Financial Data ◽  
Ensemble Classification ◽  
Classification Model ◽  
Base Classifier ◽  
C4.5 Decision Tree ◽  
Unbalanced Classification ◽  
Warning Model

Using the ensemble learning method to mine valuable information from a sea of financial data accumulated on the market of financial securities is very important for studying data processing. On the basis of financial data from A-share companies listed on Shanghai Stock Market, this article takes the perspective of unbalanced classification of ST stocks to carry out a study of the construction of a financial warning model for the listed companies. In our experiment, HDRF (HDRandom Forest, Hellinger Distance based Random Forest), ensemble classification models of Bagging, AdaBoost, and Rotation Forest, which take Hellinger distance decision tree (HDDT) as the base classifier, and the ensemble classification model which takes the C4.5 decision tree as the base classifier, are compared in respect of both the area under the ROC curve and the F-measure. As shown in the experimental results, the HDRF and the HDDT based classifier, as an ensemble method, are effective for financial data of listed companies.

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