Many binary classifiers vs. single multiclass classifier

Your Option 1 may not be the best way to go; if you want to have multiple binary classifiers try a strategy called One-vs-All.

In One-vs-All you essentially have an expert binary classifier that is really good at recognizing one pattern from all the others, and the implementation strategy is typically cascaded. For example:

  if classifierNone says is None: you are done
  else:
    if classifierThumbsUp says is ThumbsIp: you are done
    else:
      if classifierClenchedFist says is ClenchedFist: you are done
      else:
        it must be AllFingersExtended and thus you are done

Here is a graphical explanation of One-vs-all from Andrew Ng’s course:

Multi-class classifiers pros and cons:

Pros:

• Easy to use out of the box
• Great when you have really many classes

Cons:

• Usually slower than binary classifiers during training
• For high-dimensional problems they could really take a while to converge

Popular methods:

• Neural networks
• Tree-based algorithms

One-vs-All classifiers pros and cons:

Pros:

• Since they use binary classifiers, they are usually faster to converge
• Great when you have a handful of classes

Cons:

• It is really annoying to deal with when you have too many classes
• You really need to be careful when training to avoid class imbalances that introduce bias, e.g., if you have 1000 samples of none and 3000 samples of the thumbs_up class.

Popular methods:

• SVMs
• Most ensemble methods
• Tree-based algorithms