Efficient Machine Learning Technique for Web Page Classification
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- 作者：S. Markkandeyan ; M. Indra Devi
- 关键词：Web page classification ; Feature selection ; Attribute ; selected classifier ; Principal component analysis ; Genetic search ; Rank search
- 刊名：Arabian Journal for Science and Engineering
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：40
- 期：12
- 页码：3555-3566
- 全文大小：649 KB
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M. Indra Devi (2)
1. Department of Information Technology, Ratnavel Subramaniam College of Engineering and Technology, Dindigul, Tamilnadu, 624005, India
2. Department of Computer Science and Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, Tamilnadu, India - 刊物类别：Engineering
- 刊物主题：Engineering, general
Mathematics
Science, general - 出版者：Springer Berlin / Heidelberg
文摘
Web page classification plays a major role in information management and retrieval task. Feature selection is an important process for accurate classification of Web pages. Web pages contain several features, and more number of features reduce the classification accuracy. We propose a hybrid feature selection approach which is both efficient and effective for automatic Web page classification problem and also helps the Web search tool to get relevant results in the relevant category. Experiments were conducted by us with various feature selection methods for Web page classification and keyword search problem. From these experiments, it was found that some features present in the initial feature set (IFS) are irrelevant, redundant, and noisy, and they consume more memory space, increase computational time, and give a poor predictive performance. These features can be eliminated using evaluator methods such as principal component analysis, consistency subset evaluator, and search methods such as genetic search and rank search, resulting in minimal and more relevant features. We call these features as intermediate feature set (IMFS), and further optimization in this feature set gives more accurate results. Finally, attribute-selected classifier which is a part of machine learning meta-classifier was applied to the IMFS to get final feature set (FFS), and it was found that accuracy has increased up to 97% and computational time for all classifiers is minimized compared to IFS using WebKb (Faculty and Course) and ODP (Sports) benchmarking datasets. The proposed method yields better classification performance and reduces space requirements and search time in the Web documents compared with the existing methods. Keywords Web page classification Feature selection Attribute-selected classifier Principal component analysis Genetic search Rank search