Name
Abstract | ||
|---|---|---|
Traditional accuracy-based XCS classifier system generally learns and evolves classifiers from scratch when facing each particular problem. Inspired by humans with the ability to learn new skills by inducing knowledge from related problems, transfer learning (TL) focuses on leveraging the knowledge of source domains to help the problem solving of another different but related domain. This paper attempts to combine XCS and TL to propose a novel extension transferable XCS (tXCS). tXCS utilizes the inherent characteristics of XCS, that naturally discovers expressive classifiers as the generalized knowledge of domains, to realize the classifier transfer from source domains to a target domain that makes it learn faster, which is conceptually different from the previous integrations between XCS and TL. The systematic study is presented to verify the ability of knowledge transfer between domains with different degrees of similarity, which has been pointed out to be the challenge of TL. We demonstrate that tXCS can significantly speed up the learning efficiency of canonical XCS in both of single-step and multi-step benchmark problems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2908812.2908859 | GECCO |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianneng Li | 1 | 43 | 3.30 |
| Guangfei Yang | 2 | 2 | 2.06 |