Web Content Classification: A Survey <s> III. RELATED WORK <s> The World Wide Web is growing at an uncontrollable rate. Hundreds of thousands of web sites appear every day, with the added challenge of keeping the web directories up-to-date. Further, the uncontrolled nature of web presents difficulties for Web page classification. As the number of Internet users is growing, so is the need for classification of web pages with greater precision in order to present the users with web pages of their desired class. However, web page classification has been accomplished mostly by using textual categorization methods. Herein, we propose a novel approach for web page classification that uses the HTML information present in a web page for its classification. There are many ways of achieving classification of web pages into various domains. This paper proposes an entirely new dimension towards web page classification using Artificial Neural Networks (ANN). Index Terms: World Wide Web, Web page classification, textual categorization, HTML, Artificial Neural Networks, ANN. <s> BIB001 </s> Web Content Classification: A Survey <s> III. RELATED WORK <s> Increase in the amount of information on the Web has caused the need for accurate automated classifiers for Web pages to maintain Web directories and to increase search engines' performance. As every (HTML/XML) tag and every term on each Web page can be considered as a feature, we need efficient methods to select best features to reduce feature space of the Web page classification problem. In this study, our aim is to apply a recent optimization technique namely the firefly algorithm (FA), to select best features for Web page classification problem. The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behavior of fireflies. In this study, we use FA to select a subset of features, and to evaluate the fitness of the selected features J48 classifier of the Weka data mining tool is employed. WebKB and Conference datasets were used to evaluate the effectiveness of the proposed feature selection system. We observed that when a subset of features are selected by using FA, WebKB and Conference datasets were classified without loss of accuracy, even more, time needed to classify new Web pages reduced sharply as the number of features were decreased. <s> BIB002 </s> Web Content Classification: A Survey <s> III. RELATED WORK <s> Nowadays, the Web has become one of the most widespread platforms for information change and retrieval. As it becomes easier to publish documents, as the number of users, and thus publishers, increases and as the number of documents grows, searching for information is turning into a cumbersome and time-consuming operation. Due to heterogeneity and unstructured nature of the data available on the WWW, Web mining uses various data mining techniques to discover useful knowledge from Web hyperlinks, page content and usage log. The main uses of web content mining are to gather, categorize, organize and provide the best possible information available on the Web to the user requesting the information. The mining tools are imperative to scanning the many HTML documents, images, and text. Then, the result is used by the search engines. In this paper, we first introduce the concepts related to web mining; we then present an overview of different Web Content Mining tools. We conclude by presenting a comparative table of these tools based on some pertinent criteria. <s> BIB003 </s> Web Content Classification: A Survey <s> III. RELATED WORK <s> Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems. <s> BIB004 </s> Web Content Classification: A Survey <s> III. RELATED WORK <s> In this article; Intelligent Water Drops (IWD) algorithm is adapted for feature selection with Rough Set (RS). Specifically, IWD is used to search for a subset of features based on RS dependency as an evaluation function. The resulting system, called IWDRSFS (Intelligent Water Drops for Rough Set Feature Selection), is evaluated with six benchmark data sets. The performance of IWDRSFS are analysed and compared with those from other methods in the literature. The outcomes indicate that IWDRSFS is able to provide competitive and comparable results. In summary, this study shows that IWD is a useful method for undertaking feature selection problems with RS. <s> BIB005
In the paper BIB002 they have introduced that increase in the amount of information on the Web has caused the need for accurate automated classifiers for Web pages to maintain Web directories and to increase search engine performance. Every tag and every term on each Web page can be considered as a feature there is a need for efficient methods to select best features to reduce feature space of the Web page classification problem. The aim of this paper is to apply a recent optimization technique namely the firefly algorithm (FA) to select best features for Web page classification problem. The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behavior of fireflies.Using FA to select a subset of features and to evaluate the fitness of the selected features J48 classifier of the Weka data mining tool is employed. Web KB and Conference datasets were used to evaluate the effectiveness of the proposed feature selection system.Observation is that when a subset of features are selected by using FA, WebKB and Conference datasets were classified without loss of accuracy even more time needed to classify new Web pages reduced sharply as the number of features were decreased. In the paper BIB003 they have introduced the Web has become one of the most widespread platforms for information change and retrieval. As it becomes easier to publish documents as the number of users and thus publishers, increases and as the number of documents grows searching for information is turning into a cumbersome and time-consuming operation. Due to heterogeneity and unstructured nature of the data available on the WWW Web mining uses various data mining techniques to discover useful knowledge from Web hyperlinks page content and usage log.The main uses of web content mining are to gather categorize, organize and provide the best possible information available on the Web to the user requesting the information. The mining tools are imperative to scanning the many HTML documents, images, and text. Then the result is used by the search engines. In this paper they have firstly introduced the concepts related to web mining then present an overview of different Web Content Mining tools.Then concluded by presenting a comparative table of these tools based on some pertinent criteria. In the paper [3] they have described and evaluated methods for learning to forecast forthcoming events of interest from a corpus containing 22 years of news stories. The examples of identifying significant increases in the likelihood of disease outbreaks, deaths and riots in advance of the occurrence of these events in the world. Here the details of methods and studies including the automated extraction and generalization of sequences of events from news corpora and multiple web resources are provided. The predictive power of the approach on real-world events with held from the system is evaluated. In the paper  the author dealed with a preliminary discussion of WEB mining few key computer science contributions in the field of web mining the prominent successful applications and outlines some promising areas of future research. From very beginning the potential of extracting valuable knowledge from the Web has been quite evident. Web mining i.e. the application of data mining techniques to extract knowledge from Web content, structure and usage is the collection of technologies to fulfill this potential. Web mining is the application of data mining techniques to extract knowledge from Web data where at least one of structure or usage data is used in the mining process. Interest in Web mining has grown rapidly in its short existence both in the research and practitioner communities. In the paper BIB004 the author has described nature-inspired metaheuristic algorithms especially those based on swarm intelligence have attracted much attention in the last ten years. It describes the fundamentals of firefly algorithm together with a selection of recent publications. The discussion is optimality associated with balancing exploration and exploitation, which is essential for all methodes algorithms. By comparing with ISSN: 2231-2803 www.internationaljournalssrg.org Page 100 intermittent search strategy, the conclusion is that method such as firefly algorithm are better than the optimal intermittent search strategy. Analysization of algorithms and their implications for higher-dimensional optimization problems is done. In the paper BIB001 they have proposed an entirely new dimension towards web page classification using Artificial Neural Networks (ANN).World Wide Web is growing at an uncontrollable rate. Hundreds of thousands of web sites appear every day with the added challenge of keeping the web directories up-to-date.The uncontrolled nature of web presents difficulties for Web page classification.As the number of Internet users is growing, so there is a need for classification of web pages with greater precision in order to present the users with web pages of their desired class. However, web page classification has been accomplished mostly by using textual categorization methods. In this paper they have proposed a novel approach for web page classification that uses the HTML information present in a web page for its classification is done. In the paper BIB005 they have introduced that Intelligent Water Drops (IWD) algorithm is adapted for feature selection with Rough Set (RS). Specifically, IWD is used to search for a subset of features based on RS dependency as an evaluation function. The resulting system, called IWDRSFS (Intelligent Water Drops for Rough Set Feature Selection), is evaluated with six benchmark data sets. The performance of IWDRSFS are analysed and compared with those from other methods in the literature. The outcomes indicate that IWDRSFS is able to provide competitive and comparable results. In summary, this study shows that IWD is a useful method for undertaking feature selection problems with RS. In the paper [8] they propose a genetic algorithm to select best features for Web page classification problem to improve accuracy and run time performance of the classifiers. To determine whether a Web page belongs to a specific class (e.g., a graduate student homepage, a course page, etc.) or not, a classifier needs to have "good" features extracted from the Web pages. As every component in a Web page such as HTML tags and terms can be taken as a feature, dimension of the classification problem becomes too high to be solved by well known classifiers like decision trees, support vector machines, etc. To decrease the feature space, we developed a genetic algorithm that determines the best features for a given set of Web pages. It is found that when features selected by our genetic algorithm are used and a kNN classifier is employed, the accuracy improves up to 96%.