The multiscale classifier
The multiscale classifier
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DOI：
10.1109/34.481538
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发表时间：
1996-02-01
影响因子：
23.6
通讯作者：
Bradley, AP
中科院分区：
文献类型：
--
作者：
Lovell, BC;Bradley, AP
在本文中，我们提出了一个基于规则的归纳学习算法称为多尺度分类（MSC）。它可以应用于任何N维真实的或二进制分类问题，通过连续地将特征空间分成两半来对训练数据进行分类。该算法与现有的基于MTS的方法有几个显著的区别：学习是增量的，树是非二进制的，决策的回溯在一定程度上是可能的。然后描述了MSC算法，并将其与其他归纳学习算法进行了比较，特别是ID3，C4.5和反向传播神经网络。它的性能在一些标准的基准问题，然后讨论和相关的标准学习问题，如泛化，代表性的权力，和过度专业化。
In this paper we propose a rule-based inductive learning algorithm called Multiscale Classification (MSC). It can be applied to any N-dimensional real or binary classification problem to classify the training data by successively splitting the feature space in half. The algorithm has several significant differences from existing mts-based approaches: learning is incremental, the tree is non-binary, and backtracking of decisions is possible to some extent.The paper first provides background on current machine learning techniques and outlines some of their strengths and weaknesses. It then describes the MSC algorithm and compares it to other inductive learning algorithms with particular reference to ID3, C4.5, and back-propagation neural networks. Its performance on a number of standard benchmark problems is then discussed and related to standard learning issues such as generalization, representational power, and over-specialization.