Knowledge Based Deep Inception Model for Web Page Classification

Today more and more information on the Web makes it difficult to get domain-specific information due to the huge amount of data sources and the keywords that have few features. Anchor texts, which contain a few features of a specific topic, play an important role in domain-specific information retrieval, especially in Web page classification. However, the features contained in anchor texts are not informative enough. This paper presents a novel incremental method for Web page classification enhanced by link-contexts and clustering. Directly applying the vector of anchor text to a classifier might not get a good result because of the limited amount of features. Link-context is used first to obtain the contextual information of the anchor text. Then, a hierarchical clustering method is introduced to cluster feature vectors and content unit, which increases the length of a feature vector belonging to one specific class. Finally, incremental SVM is proposed to get the final classifier and increase the accuracy and efficiency of a classifier. Experimental results show that the performance of our proposed method outperforms the conventional topical Web crawler in Harvest rate and Target recall.

Focused Crawler collects domain specific web page from the internet. However, the performance of focused web crawler depends upon the multidimensional nature of the web page. This paper presents a comprehensive analysis of recent web page classifiers for focused crawlers and also explores the impact of web-based feature in collaboration with web classifier. It also evaluates the performance of classification technique such as Support vector machine, Naive Bayes, Linear Regression and Random Forest over web page classification. Along with that it examines the impact of web feature i.e. anchor text, Page content and link over web page classification. Finally the paper yield interesting result about the collective response of web feature and classification technique for web page classification as a relevant class and irrelevant class.