The presumption in classifier selection is that there is an oracle that can identify the best expert for a particular input x. This expert’s decision is accepted as the decision of the ensemble for x. The operation of a classifier selection example is shown in Figure 7.1.
FIGURE 7.1 Operation of the classifier selection ensemble.
We note that classifier selection is different from creating a large number of classifiers and then selecting an ensemble among these. We call the latter approach overproduce and select and discuss it in Chapter 8.
The idea of using different classifiers for different input objects was suggested by Dasarathy and Sheela back in 1979 [82]. They combine a linear classifier and a k-nearest neighbor (k-nn) classifier. The composite classifier identifies a conflict domain in the feature space and uses k-nn in that domain while the linear classifier is used elsewhere. In 1981, Rastrigin and Erenstein [322] gave a methodology for classifier selection almost in the form it is used now.
We may assume that the classifier “realizes” its competence for labeling x. For example, if the 10-nearest neighbor is used, and 9 of the 10 neighbors suggest the same class label, then the confidence in the decision is high. If the classifier outputs are reasonably well calibrated estimates of the posterior probabilities, then the ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month, and much more.
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.
Julian F.
Head of Cybersecurity
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.
Addison B.
Field Engineer
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.
Amir M.
Data Platform Tech Lead
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.
