A Block-Based Feature Selection Method for Classification of Web Pages
Keywords:Web classification, Feature extraction, Blocks Segmentation, Spam Filtering, Semantics Word, Classifier
Webpage Classification is one of the methods for retrieving useful information that can be used for many purposes like searching, organizing, and spam filtering, and so on. Most of the existing web page classification algorithms focus on extracting the entire data however recent works focus on selective retrieval that could improve the efficiency of the classification. In this paper, we propose a block-wise feature selection algorithm that can segment a web page into blocks and finally filter out all non-important blocks. We introduce three features namely 1) keyword weighting, 2) block segmentation and, 3) similarity measures for improving the efficiency of the classification process. We select blocks that are very crucial in the classification process. Since the useless blocks are removed, the feature space is reduced and the accuracy is increased. The semantic words are also eliminated and the subset of most relevant features is choosing for building the classification model. The results shows an improved classification results as the relationship between the features and the target variable is understood easily. To demonstrate the efficiency of the proposed model, we compared it with other top machine learning classifiers. Two datasets are used in our experiment. The experimental results showed that our proposed work with four machine learning models and obtained up to 95% accuracy which is 11.7% more than existing models
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