Learning Full-Sentence Co-Related Verb Argument Preferences from Web Corpora

2011 ◽  
pp. 137-162
Author(s):  
Hiram Calvo ◽  
Kentaro Inui ◽  
Yuji Matsumoto
Keyword(s):  
Machine Learning ◽  
Generative Models ◽  
Ensemble Model ◽  
Semantic Features ◽  
Data Sparseness ◽  
Task Learning

Learning verb argument preferences has been approached as a verb and argument problem, or at most as a tri-nary relationship between subject, verb and object. However, the simultaneous correlation of all arguments in a sentence has not been explored thoroughly for sentence plausibility mensuration because of the increased number of potential combinations and data sparseness. In this work the authors present a review of some common methods for learning argument preferences beginning with the simplest case of considering binary co-relations, then they compare with tri-nary co-relations, and finally they consider all arguments. For this latter, the authors use an ensemble model for machine learning using discriminative and generative models, using co-occurrence features, and semantic features in different arrangements. They seek to answer questions about the number of optimal topics required for PLSI and LDA models, as well as the number of co-occurrences that should be required for improving performance. They explore the implications of using different ways of projecting co-relations, i.e., into a word space, or directly into a co-occurrence features space. The authors conducted tests using a pseudo-disambiguation task learning from large corpora extracted from Internet.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

scholarly journals An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1285
Author(s):  
Mohammed Al-Sarem ◽  
Faisal Saeed ◽  
Zeyad Ghaleb Al-Mekhlafi ◽  
Badiea Abdulkarem Mohammed ◽  
Tawfik Al-Hadhrami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Ensemble Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Ensemble Model ◽  
Security Attacks ◽  
Data Set ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

scholarly journals SURG-02. SURVIVAL PREDICTION AFTER NEUROSURGICAL RESECTION OF BRAIN METASTASES: A MACHINE LEARNING APPROACH

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii203-ii203
Author(s):  
Alexander Hulsbergen ◽  
Yu Tung Lo ◽  
Vasileios Kavouridis ◽  
John Phillips ◽  
Timothy Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Metastases ◽  
External Validation ◽  
Superior Performance ◽  
Prognostic Models ◽  
Receiver Operating Curve ◽  
Gradient Boosting ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Adaptive Boosting

Abstract INTRODUCTION Survival prediction in brain metastases (BMs) remains challenging. Current prognostic models have been created and validated almost completely with data from patients receiving radiotherapy only, leaving uncertainty about surgical patients. Therefore, the aim of this study was to build and validate a model predicting 6-month survival after BM resection using different machine learning (ML) algorithms. METHODS An institutional database of 1062 patients who underwent resection for BM was split into a 80:20 training and testing set. Seven different ML algorithms were trained and assessed for performance. Moreover, an ensemble model was created incorporating random forest, adaptive boosting, gradient boosting, and logistic regression algorithms. Five-fold cross validation was used for hyperparameter tuning. Model performance was assessed using area under the receiver-operating curve (AUC) and calibration and was compared against the diagnosis-specific graded prognostic assessment (ds-GPA); the most established prognostic model in BMs. RESULTS The ensemble model showed superior performance with an AUC of 0.81 in the hold-out test set, a calibration slope of 1.14, and a calibration intercept of -0.08, outperforming the ds-GPA (AUC 0.68). Patients were stratified into high-, medium- and low-risk groups for death at 6 months; these strata strongly predicted both 6-months and longitudinal overall survival (p < 0.001). CONCLUSIONS We developed and internally validated an ensemble ML model that accurately predicts 6-month survival after neurosurgical resection for BM, outperforms the most established model in the literature, and allows for meaningful risk stratification. Future efforts should focus on external validation of our model.

scholarly journals Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

2017 ◽  
Vol 79 (02) ◽  
pp. 123-130 ◽  
Author(s):  
Whitney Muhlestein ◽  
Dallin Akagi ◽  
Justiss Kallos ◽  
Peter Morone ◽  
Kyle Weaver ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Patient Outcomes ◽  
Predictive Power ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Motor Deficit ◽  
Ensemble Model ◽  
Algorithm Selection ◽  
Discharge Disposition

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

scholarly journals Predicting hospitalization following psychiatric crisis care using machine learning

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Matthijs Blankers ◽  
Louk F. M. van der Post ◽  
Jack J. M. Dekker
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Ensemble Model ◽  
K Nearest Neighbors ◽  
Crisis Care

Abstract Background Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization. Methods Data from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable for the prediction models was whether the patient was hospitalized in the 12 months following inclusion. The predictive power of 39 variables related to patients’ socio-demographics, clinical characteristics and previous mental health care contacts was evaluated. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared and we also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis and the five best performing algorithms were combined in an ensemble model using stacking. Results All models performed above chance level. We found Gradient Boosting to be the best performing algorithm (AUC = 0.774) and K-Nearest Neighbors to be the least performing (AUC = 0.702). The performance of GLM/logistic regression (AUC = 0.76) was slightly above average among the tested algorithms. In a Net Reclassification Improvement analysis Gradient Boosting outperformed GLM/logistic regression by 2.9% and K-Nearest Neighbors by 11.3%. GLM/logistic regression outperformed K-Nearest Neighbors by 8.7%. Nine of the top-10 most important predictor variables were related to previous mental health care use. Conclusions Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was in most cases modest. The results show that a predictive accuracy similar to the best performing model can be achieved when combining multiple algorithms in an ensemble model.

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

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