Ensemble Methods for Multi-label Classification

07/06/2013
by   Lior Rokach, et al.
0

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared with the RAKEL algorithm and to other state-of-the-art algorithms.

READ FULL TEXT

page 23

page 24

page 26

research
01/07/2018

Applying an Ensemble Learning Method for Improving Multi-label Classification Performance

In recent years, multi-label classification problem has become a controv...
research
01/25/2019

Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification

The aim of this paper was to compare soft confusion matrix approach and ...
research
03/08/2014

Multi-label ensemble based on variable pairwise constraint projection

Multi-label classification has attracted an increasing amount of attenti...
research
08/16/2020

SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning

Although significant progress achieved, multi-label classification is st...
research
10/08/2019

Self-Paced Multi-Label Learning with Diversity

The major challenge of learning from multi-label data has arisen from th...
research
11/22/2018

Generalized Range Moves

We consider move-making algorithms for energy minimization of multi-labe...
research
07/08/2021

Parameter Selection: Why We Should Pay More Attention to It

The importance of parameter selection in supervised learning is well kno...

Please sign up or login with your details

Forgot password? Click here to reset