首页> 美国卫生研究院文献>BMC Medical Informatics and Decision Making >CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

【2h】

CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

机译：CNFE-SE：一种组合复杂的基于网络的特征工程和堆叠合奏的新方法以预测宫内授精和排名的成功

代理获取

本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。

页面导航

摘要
著录项
相似文献
相关主题

摘要

Summarizing the previous studies of predicting ART outcome

机译：总结了以前的预测艺术结果研究

著录项

期刊名称 BMC Medical Informatics and Decision Making
作者
Sima Ranjbari; Toktam Khatibi; Ahmad Vosough Dizaji; Hesamoddin Sajadi; Mehdi Totonchi; Firouzeh Ghaffari;
展开▼
作者单位

展开▼
年(卷),期 2021(-1),-1
年度 2021
页码 -1
总页数 29
原文格式 PDF
正文语种
中图分类情报学;
关键词
IUI outcome prediction; Complex networks; Feature engineering; Stacked ensemble classifier; Feature selection;

机译：IUI结果预测;复杂网络;特征工程;堆叠合奏分类器;特征选择;
入库时间 2022-08-21 11:55:37

相似文献

外文文献
中文文献
专利

1. CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features [J] . Sima Ranjbari, Toktam Khatibi, Ahmad Vosough Dizaji, BMC Medical Informatics and Decision Making . 2021,第1期

机译：CNFE-SE：一种新的方法，结合复杂的网络的特征工程和堆叠集合来预测宫内授精和排名的成功
2. Ensemble feature selection approach based on feature ranking for rice seed images classification [J] . Tuan Dzi Lam Tran, Surinwarangkoon Thongchai, Meethongjan Kittikhun, Advances in Electrical and Electronic Engineering . 2020,第3期

机译：基于稻米图像分类的特征排名的合奏特征选择方法
3. Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach [J] . Ursula Neumann, Mona Riemenschneider, Jan-Peter Sowa, BioData Mining . 2016,第1期

机译：通过使用一种新颖的整体特征选择方法来补偿特征选择偏差并改善二进制分类的预测性能
4. A Stacked Ensemble Learning Framework with Heterogeneous Feature Combinations for Predicting ncRNA-Protein Interaction [C] . Qiguo Dai, Zhaowei Wang, Jinmiao Song, IEEE International Conference on Bioinformatics and Biomedicine . 2020

机译：具有异质特征组合的堆叠集合学习框架，用于预测NCRNA蛋白质相互作用
5. A Network-Based Approach for Analyzing and Predicting Customer-Product Relations in Engineering Desig. [D] . Wang, Mingxian. 2016

机译：一种基于网络的方法，用于分析和预测工程设计中的客户-产品关系。
6. Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach [O] . Ursula Neumann, Mona Riemenschneider, Jan-Peter Sowa, 2016

机译：通过使用新颖的集成特征选择方法补偿特征选择偏差并改善二进制分类的预测性能
7. CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features [O] . Sima Ranjbari, Toktam Khatibi, Ahmad Vosough Dizaji, 2021

机译：CNFE-SE：一种组合复杂的基于网络的特征工程和堆叠合奏的新方法，以预测宫内授精和排名的成功

CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

摘要

著录项

相似文献

相关主题

期刊订阅