19/10/2020

MetaTPOT: Enhancing a tree-based pipeline optimization tool using meta-learning

Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach

Keywords: tpot, meta-learning, genetic programming(gp), automl

Abstract Paper Similar Papers

Abstract: Automatic machine learning (AutoML) aims to automate the different aspects of the data science process and, by extension, allow non-experts to utilize "off the shelf" machine learning solution. One of the more popular AutoML methods is the Tree-based Pipeline Optimization Tool (TPOT), which uses genetic programming (GP) to efficiently explore the vast space of ML pipelines and produce a working ML solution. However, TPOT’s GP process comes with substantial time and computational costs. In this study, we explore TPOT’s GP process and propose MetaTPOT, an enhanced variant that uses a meta learning-based approach to predict the performance of TPOT’s pipeline candidates. MetaTPOT leverages domain knowledge in the form of pipelines pre-ranking to improve TPOT’s speed and performance. Evaluation on 65 classification datasets shows that our approach often improves the outcome of the genetic process while simultaneously substantially reduce its running time and computational cost.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3340531.3412147#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at CIKM 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 15:25 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 5:22 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 11:50 
 0
 0
 0
 0
 11:18 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 11:54 
 0
 0
 0
 0
 11:27 
 0
 0
 0
 1
 5:17 
 0
 0
 0
 0
 12:25 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 15:25 
 0
 0
 0
 0
 17:42 
 0
 0
 0
 0
 13:31 
 0
 0
 0
 0
 15:39 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 0
 7:41 
 0
 0
 0
 0
 3:06 
 0
 0
 0
 0
 5:18 
 0
 0
 0
 0
 7:18 
 0
 0
 0
 0
 15:47 
 0
 0
 0
 0
 14:25 
 0
 0
 0
 0
 15:05 
 0
 0
 0
 0
 19:42 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 0
 2:45 
 0
 0
 0
 0
 13:40 
 0
 0
 0
 0
 11:41 
 0
 0
 0
 0
 17:58 
 0
 0
 0
 0
 15:00 
 0
 0
 0
 0
 8:04 
 0
 0
 0
 0
 11:49 
 0
 0
 0
 0
 10:00 
 0
 0
 0
 0
 5:18 
 0
 0
 0
 0
 14:20 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 8:14 
 0
 0
 0
 0
 5:49 
 0
 0
 0
 0
 19:14