Comparative Study
Comparative Study
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DOI：
10.1007/978-1-4757-3296-2_7
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发表时间：
2020-02
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影响因子：
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通讯作者：
Nina Zahrai
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文献类型：
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作者：
Nina Zahrai
本节将信息理论网络的性能与数据挖掘的主要分类方法进行比较。该比较基于公共数据集，包括以下性能标准：精确度降低是通过算法删除的候选输入属性的部分（从网络中排除）以及信息理论网络与其他预测模型的大小来衡量的。预测准确度是网络在验证案例上的平均准确度与其他分类器的发布准确度。稳定性表示算法从同一数据集的不同随机样本中提供相似结果的能力。用于比较的基准分类方法包括：朴素贝叶斯分类器。这是一种假设所有输入属性条件独立的概率方法。参见（Mitchell，1997）. C4.5中的算法细节。这是（Quinlan，1993）中提出的最先进的决策树算法。今天，大多数用于构建决策树的商业工具都基于C4.5或其修改版本之一。1984年）。它被用作商业工具的引擎，具有相同的名称，可从Salford Systems（http：//www.salford-systems.com/）获得。
This section compares the performance of the information-theoretic network to leading classification methods of data mining. The comparison is based on public datasets and it includes the following performance criteria:Dimensionality Reductionis measured by the portion of candidate-input attributes removed by the algorithm (excluded from the network) and by the size of the information-theoretic network vs. other predictive models.Prediction Accuracyis the average accuracy of the network on validation cases vs. published accuracy of other classifiers.Stabilityrepresents the ability of the algorithms to provide similar results from different random samples of the same dataset. The benchmark classification methods used for the comparison include:Naive Bayes Classifier. This is a probabilistic method assuming conditional independence of all input attributes. See the details of the algorithm in (Mitchell, 1997).C4.5.This is a state-of-the-art decision tree algorithm presented in (Quinlan, 1993). Today, most commercial tools for constructing decision trees are based on C4.5 or one of its modified versions.CART ™This is an earlier decision tree method (Breiman et al., 1984). It is used as the engine of a commercial tool, having the same name, which is available from Salford Systems (http://www.salford-systems.com/).