The unimodal model for the classification of ordinal data
The unimodal model for the classification of ordinal data
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DOI：
10.1016/j.neunet.2007.10.003
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
J. Costa;Hugo Alonso;Jaime S. Cardoso
中科院分区：
文献类型：
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作者：
J. Costa;Hugo Alonso;Jaime S. Cardoso
许多现实生活中的问题需要将项目分类为自然有序的类。这些问题传统上是通过用于名义类分类的常规方法来处理的，其中忽略了顺序关系。本文介绍了一种新的机器学习范式，用于分类有序的多类分类问题。该范式的理论发展是在与给定查询相关的随机变量类应遵循单峰分布的关键思想下进行的。在这种情况下，考虑了两种方法：参数化，其中随机变量类被假设遵循特定的离散分布；一种非参数的，其中随机变量类被假定为无分布的。在任何一种情况下，单峰模型都可以通过前馈神经网络和支持向量机等方法在实践中实现。然而，我们主要关注的是前馈神经网络。我们还引入了一个新的系数print来衡量有序数据分类器的性能。为了说明参数方法和非参数方法的性能，并将它们与其他方法的性能进行比较，本文给出了人工数据集和真实数据集的实验研究。指出了参数方法的优越性，即在考虑柔性离散分布（本文引入的一个新概念）时。
Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes where the order relation is ignored. This paper introduces a new machine learning paradigm intended for multi-class classification problems where the classes are ordered. The theoretical development of this paradigm is carried out under the key idea that the random variable class associated with a given query should follow a unimodal distribution. In this context, two approaches are considered: a parametric, where the random variable class is assumed to follow a specific discrete distribution; a nonparametric, where the random variable class is assumed to be distribution-free. In either case, the unimodal model can be implemented in practice by means of feedforward neural networks and support vector machines, for instance. Nevertheless, our main focus is on feedforward neural networks. We also introduce a new coefficient, rint, to measure the performance of ordinal data classifiers. An experimental study with artificial and real datasets is presented in order to illustrate the performances of both parametric and nonparametric approaches and compare them with the performances of other methods. The superiority of the parametric approach is suggested, namely when flexible discrete distributions, a new concept introduced here, are considered.