User:Kundan2510/sandbox

The Multi-label classification methods that adapt, extend and customize an existing machine learning algorithm for the task of multi-label learning are called algorithm adaptation methods. There are many such methods based on following machine learning algorithms : boosting, k-nearest neighbors, decision trees and neural networks. The extended methods are able to directly handle multi-label data.

List of algorithm adaptation methods for multi-label classification

 * Boosting: AdaBoost.MH and AdaBoost.MR are extended versions of AdaBoost for multi-label data.
 * k-nearest neighbors: A lazy learning approach to multi-label learning (ML-kNN) has extended k-Nearest neighbors method.
 * decision trees: Clare adapted C4.5 algorithm for multi-label classification by modifying formula for entropy calculation.
 * neural networks: BP-MLL is an adaptation of the popular back-propagation algorithm for multi-label learning.