CODEWITHSUNDEEP
machine-learningclassification
user2204574
user2204574

Reputation: 25

Late fusion step of classification using libLinear

I am doing a classification work that use libLinear as kernel these days. And have trained two type of feature sets into two models to do prediction for a query input. Wish to utilize Late Fusion to combine two result from models, I change the code of liblinear that I can get the decision score for different classes. So we got two sets of score to determine which class the query should be in.

Is there any standard way to do this "Late Fusion" or just intuitively add two scores of each classes and choose the class with highest score as candidate?

Upvotes: 0

Views: 2890

Answers (1)

zlyde
zlyde

Reputation: 61

The standard way to combine multiple classifiers would be a weighted sum of the scores of the individual classifiers. Of course, you then have the problem of specifying the weight coefficients. There are different possibilities:

  • set weights uniformly
  • set weights proportional to performance of classifier
  • train a new classifier which takes the scores as input

Upvotes: 3

Related Questions