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Cited by 96Domain Adaptation and Few-Shot LearningData Stream Mining TechniquesMachine Learning and Algorithms

The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.

Learning Cumulatively to Become More Knowledgeable

Open access
Fei, Geli, Wang, Shuai, Liu, Bing·2016·Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
DOI 10.1145/2939672.2939835
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Abstract

In classic supervised learning, a learning algorithm takes a fixed training data of several classes to build a classifier. In this paper, we propose to study a new problem, i.e., building a learning system that learns cumulatively. As time goes by, the system sees and learns more and more classes of data and becomes more and more knowledgeable. We believe that this is similar to human learning. We humans learn continuously, retaining the learned knowledge, identifying and learning new things, and updating the existing knowledge with new experiences. Over time, we cumulate more and more knowledge. A learning system should be able to do the same. As algorithmic learning matures, it is time to tackle this cumulative machine learning (or simply cumulative learning) problem, which is a kind of lifelong machine learning problem. It presents two major challenges. First, the system must be able to detect data from unseen classes in the test set. Classic supervised learning, however, assumes all classes in testing are known or seen at the training time. Second, the system needs to be able to selectively update its models whenever a new class of data arrives without re-training the whole system using the entire past and present training data. This paper proposes a novel approach and system to tackle these challenges. Experimental results on two datasets with learning from 2 classes to up to 100 classes show that the proposed approach is highly promising in terms of both classification accuracy and computational efficiency.

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  • The current dominant machine learning paradigm makes the closed-world assumption, meaning that classes seen in testing or deployment must have been seen during training.

    “The current dominant machine learning paradigm (ML) makes the closedworld assumption , which means that the classes of objects seen by the system in testing or deployment must have been seen during training”

    From Open-world continual learning: Unifying novelty detection and continual learning (2025) · cites this paper as [14]

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Research works printed but not linked: 7

  1. 10.5555/647371.723908 · DOI
  2. 10.5555/188490.188586 · DOI
  3. 10.5555/2898607.2898816 · DOI
  4. 10.1023/B:AIRE.0000045502.10941.a9 · DOI · Paper page
  5. 10.5555/1394399 · DOI
  6. 10.5555/2891460.2891580 · DOI
  7. 10.5555/895612 · DOI

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Metadata

Authors
Fei, G., Wang, S., & Liu, B.
Affiliations
University of Illinois Chicago (US)
ORCID
Venue
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher
ACM
Published
2016-08-13
Pages
1565-1574
DOI
10.1145/2939672.2939835
OpenAlex
W2391561458
Cited by
95
References
48
Citations by year
2026: 2 · 2025: 5 · 2024: 10 · 2023: 12 · 2022: 14 · 2021: 16 · 2020: 10 · 2019: 14 · 2018: 5 · 2017: 5 · 2016: 2
FWCI
4.54
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor, 365 d)
Locations
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
Funders
  • National Science Foundation (IIS-1407927)
  • Bosch (gift)
  • National Cancer Institute (R01CA192240)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
cumulative learning, lifelong machine learning, unseen class detection, incremental model updating, knowledge retention

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