Towards an automated classification method for ureteroscopic kidney stone images using ensemble learning

2020 ◽  
Author(s):  
Adriana Martinez ◽  
Dinh-Hoan Trinh ◽  
Jonathan El Beze ◽  
Jacques Hubert ◽  
Pascal Eschwege ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Kidney Stone ◽  
Classification Method ◽  
Automated Classification

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

A Classification method of Fake News based on Ensemble Learning

2020 ◽  
Author(s):  
Sae Bom Lee ◽  
Joon Shik Lim ◽  
Jin Soo Cho ◽  
Sang Yeob Oh ◽  
Taeg Keun Whangbo ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Classification Method ◽  
Fake News

Automated Classification Method for Crime Scene Sketches

2020 ◽  
Vol 57 (4) ◽  
pp. 041009
Author(s):  
王凯旋 Wang Kaixuan ◽  
李卓容 Li Zhuorong ◽  
王晓宾 Wang Xiaobin ◽  
严圣东 Yan Shengdong ◽  
唐云祁 Tang Yunqi
Keyword(s):  
Crime Scene ◽  
Classification Method ◽  
Automated Classification

scholarly journals Semi-automated classification method addressing marine strategy framework directive (MSFD) zooplankton indicators

2016 ◽  
Vol 71 ◽  
pp. 398-405 ◽  
Author(s):  
Laura Uusitalo ◽  
Jose A. Fernandes ◽  
Eneko Bachiller ◽  
Siru Tasala ◽  
Maiju Lehtiniemi
Keyword(s):  
Classification Method ◽  
Automated Classification ◽  
Marine Strategy Framework Directive

scholarly journals Detecting turbulent structures on single Doppler lidar large datasets: an automated classification method for horizontal scans

2020 ◽  
Author(s):  
Ioannis Cheliotis ◽  
Elsa Dieudonné ◽  
Hervé Delbarre ◽  
Anton Sokolov ◽  
Egor Dmitriev ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Large Datasets ◽  
Supervised Machine Learning ◽  
Classification Method ◽  
City Centre ◽  
Classification Error ◽  
Doppler Lidar ◽  
Automated Classification ◽  
Wind Fields ◽  
Turbulent Structures

Abstract. Turbulent structures can be observed using horizontal scans from single Doppler lidar or radar systems. Despite the ability to detect the structures manually on the images, this method would be time-consuming on large datasets, thus limiting the possibilities to perform studies of the turbulent structures properties over more than a few days. In order to overcome this problem, an automated classification method was developed, based on the observations recorded by a scanning Doppler lidar (LEOSPHERE WLS100) and installed atop a 75-m tower in Paris city centre (France) during a 2-months campaign (September-October 2014). The lidar recorded 4577 quasi-horizontal scans for which the turbulent component of the radial wind speed was determined using the velocity azimuth display method. Three turbulent structures types were identified by visual examination of the wind fields: unaligned thermals, rolls and streaks. A learning ensemble of 150 turbulent patterns was classified manually relying on in-situ and satellite data. The differences between the three types of structures were highlighted by enhancing the contrast of the images and computing four texture parameters (correlation, contrast, homogeneity and energy) that were provided to the supervised machine learning algorithm (quadratic discriminate analysis). Using the 10-fold cross validation method, the classification error was estimated to be about 9.2 % for the training ensemble and 3.3 % in particular for streaks. The trained algorithm applied to the whole scan ensemble detected turbulent structures on 54 % of the scans, among which 34 % were coherent turbulent structures (rolls, streaks).

scholarly journals Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254047
Author(s):  
Yi Guo ◽  
Xiaolan Wang ◽  
Yongmao Huang ◽  
Liang Xu
Keyword(s):  
Ensemble Learning ◽  
Traffic Safety ◽  
Clustering Algorithms ◽  
Autonomous Driving ◽  
Classification Performance ◽  
Majority Voting ◽  
Classification Method ◽  
Driving Style ◽  
Driving Behaviors ◽  
Stable Performance

The classification of driving styles plays a fundamental role in evaluating drivers’ driving behaviors, which is of great significance to traffic safety. However, it still suffers from various challenges, including the insufficient accuracy of the model, the large amount of training parameters, the instability of classification results, and some others. To evaluate the driving behaviors accurately and efficiently, and to study the differences of driving behaviors among various vehicle drivers, a collaborative driving style classification method, which is enabled by ensemble learning and divided into pre-classification and classification, is proposed in this paper. In the pre-classification process, various clustering algorithms are utilized compositely to label some typical initial data with specific labels as aggressive, stable and conservative. Then, in the classification process, other unlabeled data can be classified accurately and efficiently by the majority voting ensemble learning method incorporating three different conventional classifiers. The availability and efficiency of the proposed method are demonstrated through some simulation experiments, in which the proposed collaborative classification method achieves quite good and stable performance on driving style classification. Particularly, compared with some other similar classification methods, the evaluation indicators of the proposed method, including accuracy, precision, recall and F-measure, are improved by 1.49%, 2.90%, 5.32% and 4.49% respectively, making it the best overall performance. Therefore, the proposed method is much preferred for the autonomous driving and usage-based insurance.

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