关于adaboost算法 [英] About adaboost algorithm

问题描述

我正在进行交通流量预测，可以预测某个地方的交通繁忙或交通繁忙.我将每种流量分类为1-5，其中1是最轻的流量，5是最重的流量.

I'm working on a traffic flow prediction where I can predict that a place has heavy or light traffic. I have classified each traffic as 1-5, 1 being the lightest traffic and 5 being the heaviest traffic.

我访问了该网站 http://www.waset. org/journals/waset/v25/v25-36.pdf ，AdaBoost算法，我真的很难学习该算法. 特别是在S是集合((<xi，yi)，i=(1,2,…,m))的部分中.其中Y={-1,+1}.什么是x，y和常量L? L的值是什么?

I came across to this website http://www.waset.org/journals/waset/v25/v25-36.pdf, AdaBoost algorithm, and I'm really having a difficulty learning this algorithm. Specially in the part where S is the set ((xi, yi), i=(1,2,…,m)). where Y={-1,+1}. What are x, y and the constant L? what is the value of L?

有人可以向我解释此算法吗? :)

Can someone explain me this algorithm? :)

推荐答案

S={(x1,y1),...,(xm,ym)}:每对(x,y)对都是用于训练(或测试)分类器的样本:

S={(x1,y1),...,(xm,ym)}: Every (x,y) pair is a sample used for training (or testing) your classifier:

• x =描述此特定样本的功能，例如列出amount of cars on the road，day of the week等的值
• y =特定x的标签，在您的情况下可以为1, 2, 3, 4 or 5

• x = The features which describe this particular sample, for example values which list the amount of cars on the road, day of the week, etc
• y = The label for a particular x, which in your case can be 1, 2, 3, 4 or 5

Table 1显示了他们使用的x功能，即:DAY，TIME，INT，DET，LINK，POS，GRE，，VOL和OCC.该表的最后一列显示标签(y)，它们将其设置为1或-1(即yes或no).表格中的每一行都是1个样本.

Table 1 in the paper shows the x features they used , namely: DAY, TIME, INT, DET, LINK, POS, GRE, DIS, VOL and OCC. The last column of the table shows the label (y), which they set to either 1 or -1 (i.e., yes or no). Every row in the table is 1 sample.

L是AdaBoost训练弱学习者的回合数量(在论文中，Random Forests被用作弱分类器).如果将L设置为1，则AdaBoost将运行1轮，并且仅训练1个弱分类器，这将导致不良结果.对L使用不同的值进行多次实验，以找到最佳值(即AdaBoost收敛时或开始过度拟合时).

L is the amount of rounds in which AdaBoost trains a weak learner (in the paper Random Forests is used as the weak classifier). If you set L to 1 then AdaBoost will run 1 round and only 1 weak classifier will be trained, which will have bad results. Perform multiple experiments with different values for L to find the optimal value (i.e., when AdaBoost is converged or when it starts to overfit).

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