Dynamic k-NN with Attribute Weighting for Automatic Web Page Classification(Dk-NNwAW)
Dynamic k-NN with Attribute Weighting for Automatic Web Page Classification(Dk-NNwAW)
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DOI：
10.5120/9321-3554
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
M. Gupta
中科院分区：
文献类型：
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作者：
M. Gupta
在过去的十五年里，互联网一直处于爆炸性扩张的状态。大量作者在万维网上添加了大量关于过多主题的网页，留下了组织这些网页以改进搜索结果从而产生更相关信息的问题。本文提出了一种改进的属性加权动态k-Nearest Neighbor分类算法。本文提出了一种基于自适应动态分类算法的WWW网页自动分类方法。网页分类的基础上的类分布的网页在他们的邻居。属性加权主要用于在类分布不平衡的情况下提高分类精度。实验结果表明，该方法具有较好的分类精度，同时改善了传统kNN分类模型的其他缺点。一般术语您的一般术语必须是任何可以用于提交材料的一般分类的术语，例如模式识别，安全性，算法等。al.
The Internet has been in a state of explosive expansion over the last decade and a half. The addition of numerous web pages to the World Wide Web by a vast array of authors on a plethora of topics leaves behind the problem of organizing these web pages in order to improve search results leading to more relevant information. In this paper, a modified attribute weighted dynamic k-Nearest Neighbor classification algorithm, using k-Means clustering, is proposed. This presents a solution to the automatic classification of Web Pages on the WWW, supported by the adaptive dynamic nature of the algorithm. Web pages are classified based on the class distribution of the pages in their neighborhood. Attribute weighting is used primarily to improve classification accuracy in cases of imbalanced class distribution. Empirical results observed show good classification accuracy, while at the same time, improving on other shortcomings of the traditional kNN classification model. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.