Distributed volunteer computing for solving ensemble learning problems
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- 作者：Eugenio Cesario ; cesario@icar.cnr.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Carlo Mastroianni mastroianni@icar.cnr.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Domenico Talia talia@dimes.unical.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Ensemble learning ; Distributed data mining ; Peer-to-peer ; Volunteer computing
- 刊名：Future Generation Computer Systems
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：54
- 期：Complete
- 页码：68-78
- 全文大小：1392 K
文摘
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We present Mining@Home, a framework for distributed data mining.
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Mining@Home combines the benefits of P2P protocols with those of the volunteer computer paradigm.
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The framework is used to discover classifiers by applying the “bagging” technique on real data.
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We present performance results showing the efficiency and scalability of our approach.
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Performance results are obtained through real experiments, simulation and analytical assessment.