Efficient Two Stage Voting Architecture for Pairwise Multi-label Classification

  title={Efficient Two Stage Voting Architecture for Pairwise Multi-label Classification},
  author={Gjorgji Madjarov and D. Gjorgjevikj and Tomche Delev},
  booktitle={Australasian Conference on Artificial Intelligence},
  • Gjorgji Madjarov, D. Gjorgjevikj, Tomche Delev
  • Published in
    Australasian Conference on…
  • Computer Science
  • A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in classification problems with large number of labels. To tackle this problem we propose a two stage voting architecture (TSVA) for efficient pair-wise… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    A Review on Multi-Label Learning Algorithms
    • 1,429
    • PDF
    Lift: Multi-Label Learning with Label-Specific Features
    • 182
    • PDF
    Two Stage Classifier Chain Architecture for efficient pair-wise multi-label learning
    • 6
    • PDF
    Lift: Multi-Label Learning with Label-Specific Features
    • 105
    • PDF
    Multi-label Learning with Label-Specific Features via Clustering Ensemble
    • 5
    A Novel Stacking Method for Multi-label Classification
    • 2
    Structured feature for multi-label learning


    Publications referenced by this paper.
    Learning multi-label scene classification
    • 1,604
    • PDF
    Round Robin Classification
    • 434
    • PDF
    A kernel method for multi-labelled classification
    • 1,097
    • PDF
    Probability Estimates for Multi-class Classification by Pairwise Coupling
    • 1,785
    • PDF
    Efficient Pairwise Classification
    • 97
    • PDF
    BoosTexter: A Boosting-based System for Text Categorization
    • 2,172
    • PDF
    AI 2010: Advances in Artificial Intelligence 3rd Australasian Joint Conference
    • 2010