Multilabel Classification Algorithm

A Multilabel Classification Algorithm is a classification algorithm that can solve a multilabel classification task (with more than one target feature).

• AKA: Multivariate Classifier.
• Context:
  • It can produce a Multilabel Classifier.
  • It can range from being a Heuristic Multilabel Classification Algorithm to being a Data-Driven Multilabel Classification Algorithm (such as a Supervised Multilabel Classification Algorithm).
• Example(s):
  • a Multilabel Text Classification Algorithm.
  • a Multilabel Naive Bayes.
  • …
• Counter-Example(s):
  • a Univariate Classification Algorithm.
  • a Multiclass Classification Algorithm, that learns a Multiclass Classifier.
  • a Multivalue Regression Algorithm.
  • a Clustering Algorithm.
• See: Multivariate Classification Algorithm.

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References

2006

• (Zhang & Zhou, 2006) ⇒ Min-Ling Zhang, Zhi-Hua Zhou. (2006). “Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization." IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1338-1351, October, (2006). doi:10.1109/TKDE.2006.162.

2004

• (Boutell et al., 2004) ⇒ Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown. (2004). “Learning Multi-label Scene Classification.” In: Pattern recognition Journal, 37(9). doi:10.1016/j.patcog.2004.03.009

• (Rifkin & Klatau, 2004) ⇒ Ryan Rifkin, and Aldebaro Klautau. (2004). “In Defense of One-Vs-All Classification.” In: The Journal of Machine Learning Research, 5.
  • QUOTE: … if the labels are independent, the problem very naturally decomposes into [math]\displaystyle{ N }[/math] unlinked binary problems, where the i^th binary learner simply learns to distinguish whether or not an example is in class i. If the labels are dependent, then how best to perform multiclass classification is an interesting research problem, but is beyond the scope of this paper.

2002

• (Elisseeff & Weston, 2002) ⇒ A. Elisseeff, and J. Weston. (2002). “A Kernel Method for Multi-Labelled Classification.” In: Advances in Neural Information Processing Systems (NIPS 2002).

1999

• (McCallum, 1999) ⇒ Andrew McCallum. (1999). “Multi-label Text Classication with a Mixture Model Trained by EM.” In: AAAI 99 Workshop on Text Learning.

2000

• (Schapire & Singer, 2000) ⇒ Robert E. Schapire, and Yoram Singer. (2002). “BoosTexter: A Boosting-based System for Text Categorization.” In: Machine Learning, 39(2/3).

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