1 / 9

Feature Selection with Branch and Bound

Feature Selection with Branch and Bound. Norman Poh. Steps. Construct an ordered tree satisfying: Traverse the tree from right to left in depth-first search pattern Evaluate the criterion at each level and sort them Prune the tree.

mada
Download Presentation

Feature Selection with Branch and Bound

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Feature Selection with Branch and Bound Norman Poh

  2. Steps • Construct an ordered tree satisfying: • Traverse the tree from right to left in depth-first search pattern • Evaluate the criterion at each level and sort them • Prune the tree where Jk means k variables to be eliminated and the order of Zpis determined by a discrimination criterion The objective is to fine the optimal value: while keeping a constantlyupdated lower bound: Whenever the criterion evaluated for any node is less than the bound B, all nodes that are successors of that node also have criterion values less than B. So, we prune them.

  3. “6 choose 2”

  4. How to construct the order tree? • Determine the number of levels of the tree • In the “6 choose 2” examples, we have to eliminate 4 variables and so the number of levels is 4 • Each child node should have a number greater than it’s parent’s • The right-most leave node (the note at the last level) should reach the largest variable index (that is 6 in the “6 choose 2” example) • A number of examples are given next

  5. “5 choose 2”

  6. “5 choose 3”

  7. “6 choose 3”

  8. Your tutorial question: 1. Construct an ordered tree according to “4 choose 2” 2. Eliminate features x1, x2, x3 and x4 – calculate the criterion and sort them 3. Put the tree

  9. Reference • PATRENAHALLI M. NARENDRA and KEINOSUKE, FUKUNAGA , “A Branch and Bound Algorithm for Feature Subset Selection”, IEEE TRANSACTIONS ON COMPUTERS, VOL. C-26, NO. 9, SEPTEMBER 1977

More Related