This paper utilizes Ant-Miner - the first Ant Colony algorithm for discovering classification rules - in the field of web content mining, and shows that it is more effective than C5.0 in two sets of BBC and Yahoo web pages used in our experiments. It also investigates the benefits and dangers of several linguistics-based text preprocessing techniques to reduce the large numbers of attributes associated with web content mining.

Web page classification with an Ant Colony algorithm

Web page classification is the automated assigning of predefined subject category to the document. Automatic web page classification is one of the most essential techniques for Web Mining given that the web is a huge repository of various information including images, videos etc. And there is a need for categorization web pages to satisfy user needs. The classification of web pages into each category exclusively relies on man power which cost much time and effort. To alleviate this manually classification problem, more researchers focus on the issue of web pages classification technology. In this paper, we proposed Random Forest Classifier (RF) based on random forest method for multi-category web page classification. The proposed RF classifier can classify web pages efficiently according to their corresponding class without using other feature selection methods. We compared the accuracy of the proposed approach to decision tree classifier using in the same Yahoo web pages. The experiments have shown that the proposed approach is suitable for the multi-category web page classification.

Random forest classifier for multi-category classification of web pages

Web Page Classification (WPC) is both an important and challenging topic in data mining. The knowledge of WPC can help users to obtain useable information from the huge internet dataset automatically and efficiently. Many efforts have been made to WPC. However, there is still room for improvement of current approaches. One particular challenge in training classifiers comes from the fact that the available dataset is usually unbalanced. Standard machine learning algorithms tend to be overwhelmed by the major class and ignore the minor one and thus lead to high false negative rate. In this paper, a novel approach for Web page classification was proposed to address this problem by using an ensemble of support vector machine classifiers to perform this work. Principal Component Analysis (PCA) is used for feature reduction and Independent Component Analysis (ICA) for feature selection. The experimental results indicate that the proposed approach outperforms other existing classifiers widely used in WPC.

Web Page Classification using an ensemble of support vector machine classifiers

As the volume of web pages given by a search engine in response to a user query is huge, automatic classification of web pages into relevant categories has become the state of the art research topic. Web page classification algorithms have additional challenges since web pages have structured, semi-structured and also unstructured data. In this article, a survey of the different approaches to web page classification, its applications and some of the associated issues are presented. Key Terms – Web page classification, Web mining, Feature selection

Recent research in web page classification - a review

This paper outlines the use of Artificial Immune Recognition System (AIRS) within the field of text/document classification. Various versions of AIRS including AIRS1, AIRS2, Parallel AIRS and Modified AIRS with Fuzzy KNN are applied to classify the mode of a text's content which is organized for helping users with their organizational tasks. In this regard, 7 major features as inputs with 3 nominal values of Low, Medium, and High are chosen to classify texts into 6 organizational functionality classes. Results of experimentation on a dataset including 540 data show the fact that different versions of AIRS, performs better compared to multi-layer perceptron and radial basis function as simple neural approaches. Due to the high performance of this approach, it is expected to be successfully applicable to a wide range of content mode classification issues in decision support environment.

Organizational texts classification using artificial immune recognition systems

As the information contained in the Web is increasing, organizing this information is a necessary requirement. Classification is a way to organize the webpsilas information. While there is different ways to classify the web information, swarm intelligence is a new one in this field. This paper investigates usage of a swarm intelligence algorithm in the field of the web page classification; Focusing on Persian web pages. Ant Miner II is the used algorithm. It also propose a simple text preprocessing technique to reduce the large numbers of attributes associated with web content mining, without dealing linguistic complications. The result shows Ant Miner II and proposed preprocessing technique are efficiency in the field of the web page classification.

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Ant colony algorithm for web page classification

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Ant colony algorithm for web page classification