Supervised Multiclass Classification Algorithm: Difference between revisions
Jump to navigation
Jump to search
m (Text replacement - "]] ** " to "]]. ** ") |
m (Text replacement - "ers]] " to "er]]s ") |
||
| Line 29: | Line 29: | ||
=== 2009 === | === 2009 === | ||
* ([[Rifkin, 2009]]) ⇒ [[Ryan Rifkin]]. ([[2009]]). “[http://www.mit.edu/~9.520/spring09/Classes/multiclass.pdf Multiclass Classification].” In: MIT Course, 9.520: Statistical Learning Theory and Applications, Spring 2009. | * ([[Rifkin, 2009]]) ⇒ [[Ryan Rifkin]]. ([[2009]]). “[http://www.mit.edu/~9.520/spring09/Classes/multiclass.pdf Multiclass Classification].” In: MIT Course, 9.520: Statistical Learning Theory and Applications, Spring 2009. | ||
** QUOTE: [[OVA]] and [[AVA]] are so simple that many [[researcher|people]] [[independently invented|invented them independently]]. </s> It’s hard to write [[ML paper| | ** QUOTE: [[OVA]] and [[AVA]] are so simple that many [[researcher|people]] [[independently invented|invented them independently]]. </s> It’s hard to write [[ML paper|paper]]s about [[them]]. </s> So there’s a whole cottage industry in fancy, [[sophisticated method]]s for [[multiclass classification]]. </s> To the best of [[Ryan Rifkin|my]] knowledge, choosing [[properly tuned]] [[regularization classifier]]s ([[RLSC]], [[SVM]]) as your underlying [[supervised binary classifier|binary classifier]]s and using [[one-vs-all (OVA)]] or [[all-vs-all (AVA)]] works as well as anything else you can do. </s> If you actually have to solve a [[supervised multiclass problem|multiclass problem]], I strongly urge you to simply use [[OVA]] or [[AVA]], and not worry about anything else. </s> The [[algorithm selection|choice]] between [[OVA]] and [[AVA]] is largely [[computational-based decision|computational]]. </s> | ||
=== 2004 === | === 2004 === | ||
Latest revision as of 00:44, 19 August 2024
A Supervised Multiclass Classification Algorithm is a supervised classification algorithm that is a multiclass classification algorithm which can be implemented by a supervised multiclass classification system (to solve a supervised multiclass classification task).
- 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.
References
2009
- (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.
2004
- (Rifkin & Klatau, 2004) ⇒ Ryan Rifkin, and Aldebaro Klautau. (2004). “In Defense of One-Vs-All Classification.” In: The Journal of Machine Learning Research, 5.
2002
- (Fürnkranz, 2002) ⇒ Johannes Fürnkranz. (2002). “Round Robin Classification.” In: The Journal of Machine Learning Research, 2. doi:10.1162/153244302320884605
2001
- (Crammer & Singer, 2001a) ⇒ Koby Crammer, and Yoram Singer. (2001). “On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines.” In: The Journal of Machine Learning Research, 2.
1995
- (Dietterich & Bakiri, 1995) ⇒ Thomas G. Dietterich, and Ghulum Bakiri. (1995). “Solving Multiclass Learning Problems via Error-Correcting Output Codes.” In: Journal of Artificial Intelligence Research, 2.