Ordered Classifier Chains for Multi-label Classification
Year: 2016
Type: article
Abstract: Classifier chains method is introduced recently in multi-label classification scope as a high predictive performance technique aims to exploit label dependencies and in the meantime preserving the computational complexity in a desirable level. In this paper, we present a method for chain's order, called Ordered Classifier Chains (OCC), elaborating that the sequence of labels in the chain plays an important role in predictive performance of corresponding multi-label classifiers. OCC proposes making use of correlation of every class label with that of features. OCC renders an ordering of class labels in their descending order. Once the ordering of labels is determined, the features along with every label are fed to binary classifier. In the classifier chain model the feature space of every binary classifier is extended with the new order of labels. In order to specify association of each sample with the set of class labels, it is given to all of classifiers. Empirical evaluations include an extensive range of multi-label datasets reveal that OCC manages to improve the classification performance compared to existing approaches.
Source: Journal of Machine Intelligence
Authors: Maryam Keikha, Sattar Hashemi
Institution: Shiraz University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W1177573322