Stacked ensemble coupled with feature selection for biomedical entity extraction

Ekbal, Asif; Saha, Sriparna

doi:10.1016/j.knosys.2013.02.008

Cited by 39 publications

(19 citation statements)

References 16 publications

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“…3.3.3 (R Foundation for Statistical Computing, Vienna, Austria) to automatically generate ML model structures based on seven ML algorithms: XG Boost, deep learning (DL), distributed random forest (DRF), generalised linear model (GLM), gradient boosting machine (GBM), extremely randomized trees, and stacked ensemble (SE). SE is an ensemble method that makes final predictions by incorporating decisions made from different models trained from other algorithms 28 .…”

Section: Methodsmentioning

confidence: 99%

“…www.nature.com/scientificreports/ erate ML model structures based on seven ML algorithms: XG Boost, deep learning (DL), distributed random forest (DRF), generalised linear model (GLM), gradient boosting machine (GBM), extremely randomized trees, and stacked ensemble (SE). SE is an ensemble method that makes final predictions by incorporating decisions made from different models trained from other algorithms 28 . For the attributes, for LR model we used logit link function and iteratively reweighted least squares (IWLS) estimation which is the default algorithm in glm() function in stats v3.6.2 package.…”

Section: Patientsmentioning

confidence: 99%

See 1 more Smart Citation

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning

Kang¹,

Lee²,

Song³

et al. 2020

Sci Rep

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Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.

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Section: Methodsmentioning

confidence: 99%

Section: Patientsmentioning

confidence: 99%

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning

Kang¹,

Lee²,

Song³

et al. 2020

Sci Rep

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show abstract

“…Other popular ensemble methods include AdaBoost, Random Forest and Bagging [62]. Stacked ensembles are the simplest yet one of the most effective ensemble methods, widely used in a variety of applications [63,64]. Stacked ensemble acts as our baseline ensemble, compared with our proposed weighted ensemble based on differential evolution, a meta-heuristic weight optimization method.…”

Section: Ensemblesmentioning

confidence: 99%

Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

2020

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Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

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“…We trained a CRF model on 100% of the source domain and stacked it with another CRF trained on 90% of the folded in target domain data. Stacking employs a meta-classifier and is a popular method for constructing high performance ensembles of classifiers (Ekbal and Saha, 2013). In this case we collected the output labels from the source domain-trained CRF on target sentences and added them as features for the target domain trained CRF.…”

Section: M4: Stackmentioning

confidence: 99%

The impact of near domain transfer on biomedical named entity recognition

Collier¹,

Tran²,

Paster³

2014

Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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Current research in fully supervised biomedical named entity recognition (bioNER) is often conducted in a setting of low sample sizes. Whilst experimental results show strong performance in-domain it has been recognised that quality suffers when models are applied to heterogeneous text collections. However the causal factors have until now been uncertain. In this paper we describe a controlled experiment into near domain bias for two Medline corpora on hereditary diseases. Five strategies are employed for mitigating the impact of near domain transference including simple transference, pooling, stacking, class re-labeling and feature augmentation. We measure their effect on f-score performance against an in domain baseline. Stacking and feature augmentation mitigate f-score loss but do not necessarily result in superior performance except for selected classes. Simple pooling of data across domains failed to exploit size effects for most classes. We conclude that we can expect lower performance and higher annotation costs if we do not adequately compensate for the distributional dissimilarities of domains during learning.

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Stacked ensemble coupled with feature selection for biomedical entity extraction

Cited by 39 publications

References 16 publications

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning

Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

The impact of near domain transfer on biomedical named entity recognition

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