Statistical Mechanical Development of a Sparse Bayesian Classifier
Statistical Mechanical Development of a Sparse Bayesian Classifier
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DOI：
10.1143/jpsj.74.2233
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发表时间：
2005-02
影响因子：
1.7
通讯作者：
S. Uda;Y. Kabashima
中科院分区：
文献类型：
--
作者：
S. Uda;Y. Kabashima
从高维真实的世界数据中提取规则的需求在各个领域都在增长。然而，这种数据的可能冗余有时使得难以获得对新样本的良好推广能力。为了解决这个问题，我们提供了一个计划，减少有效的数据维修剪冗余组件的双类分类的基础上贝叶斯框架。首先，所提出的方法的潜力是在理想的情况下，使用副本方法确认。不幸的是，精确地执行该方案在计算上是困难的。因此，我们接下来开发了一种易于处理的近似算法，当系统大小很大时，该算法在理想情况下可以提供接近最佳的性能。最后，开发的分类器的有效性进行了实验检查的一个真实的世界问题的结肠癌分类，这表明，开发的方法可以实际使用。
The demand for extracting rules from high dimensional real world data is increasing in various fields. However, the possible redundancy of such data sometimes makes it difficult to obtain a good generalization ability for novel samples. To resolve this problem, we provide a scheme that reduces the effective dimensions of data by pruning redundant components for bicategorical classification based on the Bayesian framework. First, the potential of the proposed method is confirmed in ideal situations using the replica method. Unfortunately, performing the scheme exactly is computationally difficult. So, we next develop a tractable approximation algorithm, which turns out to offer nearly optimal performance in ideal cases when the system size is large. Finally, the efficacy of the developed classifier is experimentally examined for a real world problem of colon cancer classification, which shows that the developed method can be practically useful.