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首页> 外文会议>International Conference on Computational Science and Its Applications(ICCSA 2006) pt.2; 20060508-11; Glasgow(GB) >Refinement Method of Post-processing and Training for Improvement of Automated Text Classification
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Refinement Method of Post-processing and Training for Improvement of Automated Text Classification

机译：改进后处理和训练方法以改进自动文本分类

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摘要

The paper presents a method for improving text classification by using examples that are difficult to classify. Generally, researches to improve the text categorization performance are focused on enhancing existing classification models and algorithms itself, but the range of which has been limited by the feature-based statistical methodology. In this paper, we propose a new method to improve the accuracy and the performance using refinement training and post-processing. Especially, we focused on complex documents that are generally considered to be hard to classify. Our proposed method has a different style from traditional classification methods, and take a data mining strategy and fault tolerant system approaches. In experiments, we applied our system to documents which usually get low classification accuracy because they are laid on a decision boundary. The result shows that our system has high accuracy and stability in actual conditions.
机译：本文提出了一种通过使用难以分类的示例来改进文本分类的方法。通常，改善文本分类性能的研究集中于增强现有分类模型和算法本身，但是其范围受到基于特征的统计方法的限制。在本文中，我们提出了一种通过细化训练和后处理来提高准确性和性能的新方法。特别是，我们专注于通常被认为难以分类的复杂文档。我们提出的方法与传统分类方法风格不同，并采用了数据挖掘策略和容错系统方法。在实验中，我们将我们的系统应用于通常由于分类结果位于决策边界而分类准确度较低的文档。结果表明，我们的系统在实际条件下具有较高的精度和稳定性。

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