Multiple-instance learning with evolutionary instance selection

Yongshan Zhang, Jia Wu*, Chuan Zhou, Peng Zhang, Zhihua Cai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)


Multiple-Instance Learning (MIL) represents a new class of supervised learning tasks, where training examples are bags of instances with labels only available for the bags. To solve the instance label ambiguity, instance selection based MIL models were proposed to convert bag learning to traditional vector learning. However, existing MIL instance selection approaches are all based on the instances inside the bags. In this case, at the original instance space, those potential informative instances, which do not occur in the bags are discarded. In this paper, we propose a novel learning method, MILEIS (Multiple-Instance Learning with Evolutionary Instance Selection), to adaptively determine the informative instances for feature mapping. The unique evolutionary search mechanism, including instance initialization, mutation, and crossover, ensures that MILEIS can adjust itself to the data without explicit specification of functional or distributional form for the underlying model. By doing so, MILEIS can also take full advantage of those creative informative instances to help feature mapping in an accurate way. Experiments and comparisons on real-world applications demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationDatabase systems for advanced applications
Subtitle of host publication21st International Conference, DASFAA 2016 Dallas, TX, USA, April 16–19, 2016. Proceedings, Part I
EditorsShamkant B. Navathe, Weili Wu, Shashi Shekhar, Xiaoyong Du, X. Sean Wang, Hui Xiong
PublisherSpringer, Springer Nature
Number of pages13
ISBN (Electronic)9783319320250
ISBN (Print)9783319320243
Publication statusPublished - 2016
Externally publishedYes
Event21st International Conference on Database Systems for Advanced Applications, DASFAA 2016 - Dallas, United States
Duration: 16 Apr 201619 Apr 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Database Systems for Advanced Applications, DASFAA 2016
Country/TerritoryUnited States


  • Classification
  • Evolutionary machine learning
  • Feature mapping
  • Instance selection
  • Multiple-instance learning


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