Difference between binary relevance and one hot encoding?

Question

Binary relevance is a well known technique to deal with multi-label classification problems, in which we train a binary classifier for each possible value of a feature:

http://link.springer.com/article/10.1007%2Fs10994-011-5256-5

On the other side, one hot encoders (OHE) are commonly used in natural language processing to encode a categorical feature taking multiple values as a binary vector:

http://cs224d.stanford.edu/lecture_notes/LectureNotes1.pdf

Can we consider that these two concepts are the same one? Or are there technical differences?

Answer 1

Both methods are different.

1. One-Hot encoding

In one-hot encoding, vector is considered.

Above diagram represents binary classification problem.

2. Binary Relevance

In binary relevance, we do not consider vector. Following diagram represents class label generation using binary relevance method which is using scalar value.