原文:Learning from Imbalanced Classes 数据不平衡是一个非常经典的问题,数据挖掘.计算广告.NLP等工作经常遇到.该文总结了可能有效的方法,值得参考: Do nothing. Sometimes you get lucky and nothing needs to be done. You can train on the so-called natural (or stratified) distribution and sometimes it works w…

Learning from Imbalanced Classes AUGUST 25TH, 2016 If you’re fresh from a machine learning course, chances are most of the datasets you used were fairly easy. Among other things, when you built classifiers, the example classes werebalanced, meaning t…

https://www.svds.com/learning-imbalanced-classes/ 下采样即 从大类负类中随机取一部分,跟正类(小类)个数相同,优点就是降低了内存大小,速度快! http://www.tuicool.com/articles/r2ee2ie Learn more about SMOTE, see the original 2002 paper titled “ SMOTE: Synthetic Minority Over-sampling Technique “.…

8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset by Jason Brownlee on August 19, 2015 in Machine Learning Process Has this happened to you? You are working on your dataset. You create a classification model and get 90% accuracy…

问题: ICC警情数据分类不均,30+分类,最多的分类数据数量1w+条,只有10个类别数量超过1k,大部分分类数量少于100条. 解决办法: 下采样:通过非监督学习,找出每个分类中的异常点,减少数据.或者类似Dropout,对多数类进行欠采样 上采样:类似DCGAN,通过word2vec构建相似的句子,增加数据.对少数类进行过采样. 分层分类:将数据量相差不大的类别构建一个模型,第一层分类数量最多的几个类别,最后一层分类最少的几个类别. 改变权值:增加部分分类的权值,计算损失的时候增加对样本少的…

package com.aura.scala.day01 object genericClasses { def main(args: Array[String]): Unit = { val stack = new Stack[Int] stack.push() stack.push() println(stack.pop()) println(stack.pop()) } } class Stack[A] { private var elements: List[A] = Nil def p…

How to handle Imbalanced Classification Problems in machine learning? from:https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/ Introduction If you have spent some time in machine learning and data science, you would have d…

最近在学深度学习相关的东西,在网上搜集到了一些不错的资料,现在汇总一下: Free Online Books  by Yoshua Bengio, Ian Goodfellow and Aaron Courville Neural Networks and Deep Learning42 by Michael Nielsen Deep Learning27 by Microsoft Research Deep Learning Tutorial23 by LISA lab, University…

##机器学习(Machine Learning)&深度学习(Deep Learning)资料(Chapter 2)---#####注:机器学习资料[篇目一](https://github.com/ty4z2008/Qix/blob/master/dl.md)共500条,[篇目二](https://github.com/ty4z2008/Qix/blob/master/dl2.md)开始更新------#####希望转载的朋友**一定要保留原文链接**,因为这个项目还在继续也在不定期更新．希望看到…

推荐一篇英文的博客: 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset 1.不平衡数据集带来的影响 一个不平衡的两类数据集,使用准确率(accuracy)作为模型评价指标,最后得到的准确率很高,感觉结果很棒大功告成了,但再看看混淆矩阵(confusion matrix)或者少数类(样本数量少的这一类)的召回率(recall),你的心可能就拔凉拔凉的.你可能会发现少数类的样本几乎完全分错,即模型将样本基本…