A Data-Mining Approach to Identification of Risk Factors in Avia

第十届中国R会议【合肥】

50992 人浏览

已收藏收藏

3711

微信分享

视频列表

基于低秩近似的一般性增量矩阵分解框架

黄训蓬

5013

First Order Methods for Fast Linear Programming in SHUFE

邓琪

3405

DataBrain，基于R语言开发的机器学习引擎

海宜真

3776

Targeted Sampling and Pricing Strategy with Imperfect Targetabil

高频金融数据的非参数分析方法

Consistent Multiple Change-point Detection and R implementation

李亚光

4469

Great Again or Stronger Together? Sentiment Analysis About Book

黎思言

3572

Detection and Tracking

R与深度学习的应用

眼底图像自动识别与诊断

Detecting concordance and discordance changes among a series of

赖颖蕾

3550

Smart Monitoring for Complex Diseases by Collaborative Learning

“AI+慢性病管理”使精准医疗成为可能

高校创业数据分析

证券分析师的价值分析

基于车联网数据的商业价值探索

移动程序化广告

数据融合与信用风险评估

上证50成分股的“社交网络”

如何制造一次成功的投资

交通大数据分析与可视化

AI * HR：用数据改变招聘

R语言在教育大数据上的应用

大规模线上实验与机器学习

A Data-Mining Approach to Identification of Risk Factors in Avia

复杂网络置信社团结构挖掘

社会化行为数据挖掘方法及应用

医疗大数据分析

函数型数据的过程分析方法

R在客户关系管理中的应用

讯飞大数据的实践与思考

视频介绍

课程资料

评价

嘉宾介绍

主题介绍

We present a data-mining approach to incident risk factor identification and analysis using incident data from the Federal Aviation Safety Board. Timely and accurate analysis of these reports is vital to the improvement of aviation safety. But extensive manual identification of risk factors by human experts and incomplete data present daunting challenges. Frist, topical mining techniques are used to convert underutilized textual data in the form of incident narratives to serve as input. Second, data streaming algorithms allow efficient training of risk factor classification models. The results show promise of such an approach not only as a potential approach to process daily influx of incident reports but also a more efficient way of building a classification model. The identification of risk factors is an important step to closing the gap between incident reporting and safety management systems.

未上传任何附件

说点什么

—— 点击加载更多 ——

收起

为你推荐

啊哦，暂无相关推荐