I came across a kind of classification problem.
Suppose I have a dataset with labels, where each label is one of a1, a2, a3, b1, and b2. I want to make a classifier that estimates "coarse" label, a or b.
Here is a concrete example. Imagine a set of animal images labeled by the its species. We want make a classifier that predicts big classes, like mammal, reptile or fish.
Q1. Does anybody know the name of this kind of classification problem setting?
To solve the problem, we can think of several approaches.
First make the label coarse (a1->a, a2->a, b1->b,... ) and solve it as a standard classification problem.
First solve the standard classification problem, predicting a1, a2,... Then make the label coarse.
First solve the standard classification problem, predicting a1, a2,... (level-0 classifier) Then use the output scores of level-0 classifier as input, and make a level-1 classifier which outputs a, b,.. (stacked generalization approach)
Q2. What kind of approach is good for this kind of problem?
I guess the approach 1 is not optimal since part or label information is thrown away. As Marc Claesen has pointeded out, this approach 1 is good if there is an example that is obviously a member of a but not obviously a member of one of a1, a2,...