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Using table lens to interactively build classifiers

机译:使用镜台交互式地建立分类器

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摘要

Rather than induce classification rules by sophisticated algorithms, we introduce a fully interactive approach for building classifiers from large multivariate datasets based on the table lens, a multidimensional visualization technique, and appropriate interaction capabilities. Constructing classifiers is an interaction with a feedback loop. Tile domain knowledge and human perception carl be profitably included. In our approach, both continuous and categorical attributes are processed uniformly, and continuous attributes are partitioned on the Ay. Our performance evaluation with data sets from the UCI repository demonstrates that this interactive approach is useful to easily build understandable classifiers with high prediction accuracy and no required a prior knowledge about the datasets. (C) 2001 Elsevier Science Ltd. AII rights reserved. [References: 6]
机译:我们没有采用复杂的算法来归类分类规则,而是引入了一种完全交互式的方法,用于基于台透镜,多维可视化技术和适当的交互功能,从大型多元数据集构建分类器。构造分类器是与反馈循环的交互。包括领域知识和人类感知将有利可图。在我们的方法中,连续属性和分类属性均得到统一处理,并且连续属性在Ay上进行分区。我们使用UCI储存库中的数据集进行的性能评估表明,这种交互式方法对于轻松构建具有高预测准确性的可理解分类器很有用,而无需事先了解数据集。 (C)2001 Elsevier Science Ltd.版权所有。 [参考:6]

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