How does bayesopt() optimise using categorical optimizable variables?

4 次查看（过去 30 天）
James Finley
James Finley 2019-4-5
评论： James Finley 2019-4-5
I have been using the bayesopt() function to perform Bayesian optimisation for material design. I have one set of optimisable variables that are of categorical data type. I was wondering how Gaussian process regression is performed when categorical input variables are used? Is it simply a case of using one-hot encoding?
Many thanks,
James

请先登录，再回答此问题。

采纳的回答

Don Mathis
Don Mathis 2019-4-5
The bayesopt function uses a special technique to handle categorical variables. One-hot coding is not used. Instead, bayesopt encodes the categorical variable as an integer variable, and uses an ARD Gaussian Process kernel with a fixed spatial scale on that dimension that is so small that neighboring integer values have virtually no effect on each other. The value of the objective function model at x=7 has no effect on the model's value at x=8. The result is that the distinct integer values must be probed individually to learn what the objective function is at that value. One-hot coding would probably produce similar behavior but would increase the number of variables and require a constraint between them to make sure only one dimension is probed at a time.
  3 个评论
显示 1更早的评论
Don Mathis
Don Mathis 2019-4-5
Unfortunately, no. As far as I know this is a novel technique. I know of at least one other B.O. package that encodes categoricals as integers, but then it treats the integer variable like any other, estimating a kernel scale that ties neighboring values together, which to me seems inappropriate.
James Finley
James Finley 2019-4-5
Okay Don, thanks for your help.
And I agree that this other way of encoding categoricals sounds incorrect.

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Model Building and Assessment 的更多信息

标签

产品


版本

R2019a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by