Decision rules for ensembled probabilistic classifier chain for multilabel classification
Andrey Ostapets · Machine Learning and Data Analysis · 2016
This work considers using of the main types of decision rules for the multilabel classification task.The algorithm is presented as a superposition of two algorithms: a recognition operator and a decision rule.The recognition operator converts feature vectors of objects to be recognized into scores for each class.This work considers several families of algorithms to be the recognition operator: linear models (base classifiers), probabilistic classifier chain of linear models, and ensembled probabilistic classifier chain.The decision rule converts the scores into the final answers.In this survey, main types of decision rules are described and their performance for several recognition operators is also shown.It is experimentally demonstrated that the quality of the forecast of the proposed composition exceeds the quality of the base classifiers.