A k-nearest neighbor based algorithm for multi-label classification

By Min-Ling Zhang, Zhi-Hua Zhou. IEEE International Conference on Granular Computing, 2005

464 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4c06e53bda3b83bcf071f8446bd12495be22ed2b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Min-Ling Zhang
2. Eyke Hüllermeier
3. Bernhard Pfahringer
4. G. Holmes
5. Jesse Read
6. Zhi-Hua Zhou
7. Grigorios Tsoumakas
8. Ioannis P. Vlahavas

In Computer Vision and Pattern Recognition

1. Zhi-Hua Zhou
2. Bernhard Pfahringer
3. G. Holmes
4. Jesse Read
5. Eibe Frank
6. Grigorios Tsoumakas
7. Ioannis Manousos Katakis
8. Min-Ling Zhang

In Molecular Biology

1. G. Holmes
2. Bernhard Pfahringer
3. Jesse Read
4. Grigorios Tsoumakas
5. Eyke Hüllermeier
6. Zhi-Hua Zhou
7. Min-Ling Zhang
8. Ioannis P. Vlahavas

Citations in the corpus

• Multilabel classification via calibrated label ranking (2008, methodology): “The k-nearest neighbor approach to multilabel classification in (Zhang and Zhou 2005) is inspired by Bayesian reasoning.”
• Selecting critical features for data classification based on machine learning methods (2020, result): “Some of the previous researches about KNN could be found in [82–84].”
• A Review on Multi-Label Learning Algorithms (2014, methodology): “1) Multi-Label k-Nearest Neighbor (ML-kNN): The basic idea of this algorithm is to adapt k-nearest neighbor techniques to deal with multi-label data, where maximum a posteriori (MAP) rule is utilized to make prediction by reasoning with the labeling information embodied in the neighbors [189], [191]”
• Classifier chains for multi-label classification (2009, methodology): “The most common problem transformation method is the binary relevance method (BR) (Tsoumakas and Katakis 2007; Godbole and Sarawagi 2004; Zhang and Zhou 2005).”
• Multi-label Classification Using Ensembles of Pruned Sets (2008, methodology): “The most widely used approach, the Binary Method (BM) [4, 12], learns |L| binary classifiers B0, · · · , B|L|.”
• Random k -Labelsets: An Ensemble Method for Multilabel Classification (2007, background): “5], Bayesian [9], lazy learning [16] and boosting [10] learning algorithms.”
• Multi-Label Classification: An Overview (2007, methodology): “ML-k NN (Zhang & Zhou, 2005) is an adaptation of the k NN lazy learning algorithm for multi-label data.”