.png){kind=icon}
.png){kind=icon}
Return
An Evolutionary Multitasking Method for Multiclass Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/MCI.2022.3199625.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As an important research topic of machine learning, multiclass classification has wide applications ranging from computer vision to bioinformatics. A variety of multiclass classification algorithms with promising performance have been proposed. Among them, the decomposition-based algorithms have shown their competitiveness, since they transform the original problem into several easily solved binary classification sub-problems. Unlike existing decomposition-based algorithms which tackle each sub-problem independently, this paper suggests an evolutionary multitasking method, named EMT-MC, for multiclass classification, where the concept of multitasking is introduced to achieve the multiclass classifier with better quality. To be specific, in EMT-MC, each binary classification sub-problem is firstly viewed as a task. Then, during the evolution, the tasks with low performance (termed ill-solved tasks) are aided by some well-selected assisting tasks by using the evolutionary multitasking learning. This not only ensures that the useful information in assisting tasks can be transferred into those ill-solved tasks, but also helps them to achieve classifiers with higher accuracy. Numerical experiments on different multiclass classification datasets demonstrate the superiority of the proposed method over the state-of-the-art algorithms.
Keywords:
Computer vision
Machine learning algorithms
Sociology
Transforms
Machine learning
Multitasking
Distance measurement
Journal
IF:
11.2
Papers:
606
Citations:
3.1K