Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability

v1v2v3 (latest)

Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability

14 March 2024

João Manoel Herrera Pinheiro

ArXiv (abs)PDF HTML

Papers citing "Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability"

8 / 8 papers shown

Title
Machine Learning Approaches to Predict Breast Cancer: Bangladesh Perspective Taminul Islam Arindom Kundu Nazmul Islam Khan Choyon Chandra Bonik Flora Akter Md. Jihadul Islam 37 17 0 30 Jun 2022
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks Sergei Ivanov Liudmila Prokhorenkova AI4CE 100 54 0 21 Jan 2021
Gradient Boosting Neural Networks: GrowNet Sarkhan Badirli Xuanqing Liu Zhengming Xing Avradeep Bhowmik Khoa D. Doan S. Keerthi FedML 65 87 0 19 Feb 2020
Optuna: A Next-generation Hyperparameter Optimization Framework Takuya Akiba Shotaro Sano Toshihiko Yanase Takeru Ohta Masanori Koyama 681 5,985 0 25 Jul 2019
CatBoost: unbiased boosting with categorical features Anna Veronika Dorogush Gleb Gusev A. Vorobev Nikita Kazeev Andrey Gulin 88 90 0 28 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.4K 22,397 0 22 May 2017
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 856 39,703 0 09 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.3K 17,241 0 16 Feb 2016