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Boosting algorithms

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach

Tanmay Nath, Rexford S. Ahima, Prasanna Santhanam

Predicting learning achievement using ensemble learning with result explanation

Tingting Tong, Zhen Li

Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer

Aaquib Syed, Richard Adam, [ ... ], Tim Q. Duong

Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

Yuliang Pan, Diwei Liu, Lei Deng

Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka

Namal Rathnayake, Upaka Rathnayake, Tuan Linh Dang, Yukinobu Hoshino

Altered kinetics of circulating progenitor cells in cardiopulmonary bypass (CPB) associated vasoplegic patients: A pilot study

Sanhita Nandi, Uma Rani Potunuru, [ ... ], Paul Ramesh Thangaraj

Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer

Anh Quan Ngo, Linh Quy Nguyen, Van Quan Tran

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Satria Mandala, Ardian Rizal, [ ... ], Abdul Hanan Abdullah

3-Dimensional convolutional neural networks for predicting StarCraft Ⅱ results and extracting key game situations

Insung Baek, Seoung Bum Kim

Hollow-tree super: A directional and scalable approach for feature importance in boosted tree models

Stephane Doyen, Hugh Taylor, [ ... ], Michael E. Sughrue

The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market

Pejman Peykani, Mostafa Sargolzaei, [ ... ], Hamidreza Kamyabfar

NATE: Non-pArameTric approach for Explainable credit scoring on imbalanced class

Seongil Han, Haemin Jung

Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up

Aref Andishgar, Sina Bazmi, [ ... ], Fariba Ahmadizar

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