Name
Abstract | ||
|---|---|---|
In classification, the task of domain adaptation is to learn a classifier to classify target data using unlabeled data from the target domain and labeled data from a related, but not identical, source domain. Transfer classifier induction is a common domain adaptation approach that learns an adaptive classifier directly rather than first adapting the source data. However, most existing transfer classifier induction algorithms are gradient-based, so they can easily get stuck at local optima. Moreover, they usually generate only a single classifier which might fit the source data too well, which results in poor target accuracy. In this paper, we propose a population-based algorithm that can address the above two limitations. The proposed algorithm can re-initialize a population member to a promising region when the member is trapped at local optima. The population-based mechanism allows the proposed algorithm to output a set of classifiers which is more reliable than a single classifier. The experimental results show that the proposed algorithm achieves significantly better target accuracy than four state-of-the-art and well-known domain adaptation algorithms on three real-world domain adaptation problems.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3321707.3321716 | GECCO |
Keywords | Field | DocType |
Transfer Learning, Domain adaptation, Classiication, Evolutionary Computation | Population,Computer science,Domain adaptation,Artificial intelligence,Classifier (linguistics),Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6111-8 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bach Hoai Nguyen | 1 | 60 | 7.22 |
| Bing Xue | 2 | 918 | 47.57 |
| Mengjie Zhang | 3 | 3777 | 300.33 |
| Peter Andreae | 4 | 358 | 31.85 |