Work

Multi-Label Classification: Inconsistency and Class Balanced K-Nearest Neighbor

Year: 2010

Type: conference-paper

Abstract: Many existing approaches employ one-vs-rest method to decompose a multi-label classification problem into a set of 2- class classification problems, one for each class. This method is valid in traditional single-label classification, it, however, incurs training inconsistency in multi-label classification, because in the latter a data point could belong to more than one class. In order to deal with this problem, in this work, we further develop classicalK-Nearest Neighbor classifier and propose a novel Class Balanced K-Nearest Neighbor approach for multi-label classification by emphasizing balanced usage of data from all the classes. In addition, we also propose a Class Balanced Linear Discriminant Analysis approach to address high-dimensional multi-label input data. Promising experimental results on three broadly used multi-label data sets demonstrate the effectiveness of our approach.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v24i1.7769

Authors: Hua Wang, Chris Ding, Heng Huang

Institution: The University of Texas at Arlington

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: Collaborative Research: Non-negative Matrix Factorizations for Data Mining: Foundations, Capabilities, and Applications, EAGER: Collaborative Research: Cross-Domain Knowledge Transformation via Matrix Decompositions, Collaborative Research: Matrix-Model Machine Learning: Unifying Machine Learning and Scientific Computing, New Theoretical Foundations of Tensor Applications: Clustering, Error Analysis, Global Convergence, and Robust Formulations