Supervised Multiclass Classification Algorithm: Difference between revisions

Revision as of 02:26, 24 September 2021

AKA: Supervised Multinomial Prediction Method.
Context:
- It can range from being an Model-based Supervised Multiclass Classification Algorithm to being an Instance-based Supervised Multiclass Classification Algorithm.
- It can range from being a Single-Pass Supervised Multiclass Classification Algorithm to being an Iterative Supervised Multiclass Classification Algorithm.
- It can range from being a Fully-Supervised Multiclass Classification Algorithm to being an Semi-Supervised Multiclass Classification Algorithm.
- It can range from being an Imbalanced Multiclass Supervised Classification Algorithm to being a Balanced.
- It can be susceptible to the Quantity of Target Classes.
- It can range from being a Single-Algorithm Multiclass Supervised Classifier to being a Binary-based Multiclass Supervised Classifier (such as a one-vs-all supervised classifier).
- It can be used as a Supervised Graph Link Prediction Algorithm.
Example(s):
Counter-Example(s):
See: Classification Algorithm.

(Rifkin, 2009) ⇒ Ryan Rifkin. (2009). “Multiclass Classification.” In: MIT Course, 9.520: Statistical Learning Theory and Applications, Spring 2009.
- QUOTE: OVA and AVA are so simple that many people invented them independently. It’s hard to write papers about them. So there’s a whole cottage industry in fancy, sophisticated methods for multiclass classification. To the best of my knowledge, choosing properly tuned regularization classifiers (RLSC, SVM) as your underlying binary classifiers and using one-vs-all (OVA) or all-vs-all (AVA) works as well as anything else you can do. If you actually have to solve a multiclass problem, I strongly urge you to simply use OVA or AVA, and not worry about anything else. The choice between OVA and AVA is largely computational.

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 ** a [[Supervised Multi-Label Classification Algorithm]].
 * <B>See:</B> [[Classification Algorithm]].
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