Journal of Guangdong University of Technology ›› 2018, Vol. 35 ›› Issue (03): 47-53.doi: 10.12052/gdutxb.180036

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An Algorithm Based on Multi-task Multi-instance Anti-noise Learning

Li Qi-xiang1, Xiao Yan-shan1, Hao Zhi-feng2, Ruan Yi-bang1   

  1. 1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Mathematics and Big Data, Foshan University, Foshan 528000, China
  • Received:2018-03-05 Online:2018-05-09 Published:2018-04-26

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Abstract: In multi-instance learning, classification performance may be limited due to the noisy data or a scarce amount of labeled data. To solve this problem, an algorithm based on multi-task multi-instance anti-noise learning is proposed. On the one hand, in view of the noisy data, the algorithm trains a classifier by assigning the instances in bags with different weights. And the weights of instances are updated by adopting an iterative optimization framework which decreases the influence of the noisy data. On the other hand, in view of insufficient labeled data, the classifier is extended to multi-task learning to train multiple learning tasks at the same time, so that the performance of each learning task can be improved by sharing the classification information among the tasks. Extensive experiments have showed that the proposed classification framework outperforms the existing classification methods.

Key words: multi-instance learning, anti-noise, multi-task learning, correlation, classification

CLC Number: 

  • TP301.6
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