Classification in P2P Networks by Bagging Cascade RSVMs
Classification in P2P Networks by Bagging Cascade RSVMs
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发表时间：
2008
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通讯作者：
Hock Hee Ang;Vivekanand Gopalkrishnan;S. Hoi;W. Ng;Anwitaman Datta
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作者：
Hock Hee Ang;Vivekanand Gopalkrishnan;S. Hoi;W. Ng;Anwitaman Datta
P2P 中的数据挖掘任务受到可扩展性、对等动态性、异步性和数据隐私保护等问题的约束。这些挑战给在 P2P 网络中部署传统的机器学习技术带来了困难，这可能很难达到与常规集中式解决方案相当的分类精度。我们最近研究了 P2P 网络中的分类问题，并通过级联简化支持向量机 (RSVM) 提出了一种新颖的 P2P 分类方法。尽管获得了有希望的结果，但现有的解决方案在通信和计算方面都存在冗余的缺点。在本文中，我们提出了一种新方法来克服先前方法的局限性。新方法可以有效减少冗余，从而显着提高通信和计算效率，同时仍然保持与集中式解决方案和先前提出的P2P解决方案相当的良好分类精度。实验结果证明了新的P2P分类方案的可行性和有效性。
Data mining tasks in P2P are bound by issues like scalability, peer dynamism, asynchronism, and data privacy preservation. These challenges pose difficulties for deploying conventional machine learning techniques in P2P networks, which may be hard to achieve classification accuracies comparable to regular centralized solutions. We recently investigated the classification problem in P2P networks and proposed a novel P2P classification approach by cascading Reduced Support Vector Machines (RSVM). Although promising results were obtained, the existing solution has some drawback of redundancy in both communication and computation. In this paper, we present a new approach to over the limitation of the previous approach. The new method can effectively reduce the redundancy and thus significantly improve the efficiency of communication and computation, meanwhile it still maintains good classification accuracies comparable to both the centralized solution and the previously proposed P2P solution. Experimental results demonstrate the feasibility and effectiveness of the new P2P classification solution.