Efficient Decomposition Selection for Multi-class Classification

Yawen Chen

doi:10.1109/TKDE.2021.3130239

Efficient Decomposition Selection for Multi-class Classification

2023-04-01

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AI

DOI：10.1109/TKDE.2021.3130239

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Choosing a decomposition method for multi-class classification is an important trade-off between efficiency and predictive accuracy. Trying all the decomposition methods to find the best one is too time-consuming for many applications, while choosing the wrong one may result in large loss on predictive accuracy. In this paper, we propose an automatic decomposition method selection approach called D-Chooser, which is lightweight and can choose the best decomposition method accurately. D-Chooser is equipped with our proposed difficulty index which consists of sub-metrics including distribution divergence, overlapping regions, unevenness degree and relative size of the solution space. The difficulty index has two intriguing properties: 1) fast to compute and 2) measuring multi-class problems comprehensively. Extensive experiments on real-world multi-class problems show that D-Chooser achieves an accuracy of 80.56% in choosing the best decomposition method. It can choose the best method in just a few seconds, while existing approaches verify the effectiveness of a decomposition method often takes a few hours. We also provide case studies on Kaggle competitions and the results confirm that D-Chooser is able to choose a better decomposition method than the winning solutions.

Keywords:

Indexes

Matrix decomposition

Kernel

Codes

Training

Support vector machines

Probability distribution

Machine learning

multi-class classification

decomposition method

Efficient Decomposition Selection for Multi-class Classification

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