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heal-research / HeuristicLab

HeuristicLab - An environment for heuristic and evolutionary optimization
https://dev.heuristiclab.com
GNU General Public License v3.0
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Weighted evaluation of data analysis models #1636

Open HeuristicLab-Trac-Bot opened 12 years ago

HeuristicLab-Trac-Bot commented 12 years ago

Issue migrated from trac ticket # 1636

milestone: HeuristicLab 4.x Backlog | component: Problems.DataAnalysis | priority: medium

2011-09-05 16:33:38: @gkronber created the issue

It should be possible to indicate weights for observations. Benefits:

  • More important data points can be weighted more strongly
  • Outliers can be remove by setting the weight to zero
  • If we know the variance for observations we can consider non-equal variances for observations
  • We could more easily aggregate multiple observations and put a higher weight on it (the average has less variance).
HeuristicLab-Trac-Bot commented 11 years ago

2013-07-22 18:37:07: @gkronber changed priority from medium to low

HeuristicLab-Trac-Bot commented 6 years ago

2018-02-02 07:54:19: @gkronber changed priority from low to medium

HeuristicLab-Trac-Bot commented 6 years ago

2018-02-02 07:54:19: @gkronber changed milestone from HeuristicLab 3.3.x Backlog to HeuristicLab 4.x Backlog

HeuristicLab-Trac-Bot commented 6 years ago

2018-02-02 07:54:19: @gkronber edited the issue description

HeuristicLab-Trac-Bot commented 6 years ago

2018-02-02 07:54:19: @gkronber changed title from Functionality for conditional evaluation of data analysis models to Weighted evaluation of data analysis models

HeuristicLab-Trac-Bot commented 6 years ago

2018-02-02 07:54:19: @gkronber commented

This could also be implemented via weights for rows in the dataset.

Weights allow more fine grained control over evaluation. There is seemingly no other ticket for implementation of weighted evaluation of regression models. Therefore, I'm re-purposing this ticket.

HeuristicLab-Trac-Bot commented 6 years ago

2018-02-22 10:51:07: @abeham commented

I think this would be useful in surrogate assisted optimization when you want to consider better solutions with higher weight than worse solutions.