EGAL: Exploration guided active learning for TCBR
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 0302-9743, Vol: 6176 LNAI, Page: 156-170
2010
- 22Citations
- 654Usage
- 25Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations22
- Citation Indexes22
- 22
- CrossRef7
- Usage654
- Downloads593
- Abstract Views61
- Captures25
- Readers25
- 25
Conference Paper Description
The task of building labelled case bases can be approached using active learning (AL), a process which facilitates the labelling of large collections of examples with minimal manual labelling effort. The main challenge in designing AL systems is the development of a selection strategy to choose the most informative examples to manually label. Typical selection strategies use exploitation techniques which attempt to refine uncertain areas of the decision space based on the output of a classifier. Other approaches tend to balance exploitation with exploration, selecting examples from dense and interesting regions of the domain space. In this paper we present a simple but effective exploration-only selection strategy for AL in the textual domain. Our approach is inherently case-based, using only nearest-neighbour-based density and diversity measures. We show how its performance is comparable to the more computationally expensive exploitation-based approaches and that it offers the opportunity to be classifier independent. © 2010 Springer-Verlag.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=77954996103&origin=inward; http://dx.doi.org/10.1007/978-3-642-14274-1_13; http://link.springer.com/10.1007/978-3-642-14274-1_13; http://link.springer.com/content/pdf/10.1007/978-3-642-14274-1_13.pdf; https://arrow.tudublin.ie/scschcomcon/105; https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1109&context=scschcomcon; http://www.springerlink.com/index/10.1007/978-3-642-14274-1_13; http://www.springerlink.com/index/pdf/10.1007/978-3-642-14274-1_13; https://dx.doi.org/10.1007/978-3-642-14274-1_13; https://link.springer.com/chapter/10.1007/978-3-642-14274-1_13
Springer Science and Business Media LLC
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