Logistic model trees
Logistic model trees
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DOI：
10.1007/s10994-005-0466-3
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发表时间：
2005-05-01
期刊：
影响因子：
7.5
通讯作者：
Frank, E
中科院分区：
文献类型：
--
作者：
Landwehr, N;Hall, M;Frank, E
树归纳方法和线性模型是监督学习任务的流行技术，无论是用于预测名义类还是数值。为了预测数字数量，已经有人将这两种方案结合到“模型树”中，即在叶子处包含线性回归函数的树。在本文中，我们提出了一种算法，将这种思想应用于分类问题，使用逻辑回归代替线性回归。我们使用分阶段拟合过程来构建逻辑回归模型，该模型可以以自然的方式选择数据中的相关属性，并展示如何使用这种方法通过逐步改进树中更高级别构建的逻辑回归模型来构建叶上的逻辑回归模型。我们在36个基准UCI数据集上将我们的算法与其他几种最先进的学习方案的性能进行了比较，并表明它产生了准确而紧凑的分类器。
Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and numeric values. For predicting numeric quantities, there has been work on combining these two schemes into 'model trees', i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm to several other state-of-the-art learning schemes on 36 benchmark UCI datasets, and show that it produces accurate and compact classifiers.