Information retrieval task has become a difficult task due to the growing size of the web. This demands a simple method for classifying the web pages. We propose an URL based approach, as it avoids downloading the web page contents. Feature weighing methods play an important role in improving the performance of a classifier. In this paper, we explored different weighting methods and conducted various experiments on WebKB dataset. Results show that tf.mi feature weighting technique achieves F1 measure of 79% and outperforms other weighting methods, which is an improvement of 19.6% over existing works on URL based classification. © 2017 The Author(s).

Rajalakshmi R

Computer Science

School of Computer Science and Engineering

Chennai Campus

Sanjuxaviar

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Procedia Computer Science

Sentiment analysis is a method of extracting subjective information from customer reviews. The analysis helps to reveal the consumer insights about the product, a theme, or a service. In the existing literature, various methods such as BoW and TF-IDF are employed for sentiment analysis and deep learning methods are not explored much. We made an attempt to apply Word2Vec feature weighting method for this problem. We carried out various experiments for sentiment analysis on a large dataset IMDB that contains movie review. We compared various feature weighting methods and analyzed using different classifiers, and the best combination was determined. From the experimental results, we conclude that Word2Vec with SGD is the best combination for sentiment classification problem on IMDB dataset. The result shown in the paper can be used as a base for future exploration of opinioned value on any textual data. © 2019, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing Computational Intelligence in Data Mining

Comparative Evaluation of Various Feature Weighting Methods on Movie Reviews

Malware domains generated by Domain Generated Algorithms (DGA) are highly dynamic in nature. The traditional approach of blacklisting the malicious domains is a time consuming approach and are not effective, as the DGA randomly generate the domain names for the malware. For real-time applications, malware detection is to be performed on the fly and hence sophisticated techniques are in demand to address this issue. Even though various machine learning techniques are employed for this purpose, the performance of such algorithms depends on how good the features are designed. In this work, we have proposed a transfer learning technique by combining the best performing Convolutional Neural Network with the machine learning algorithms such as Naive Bayes classifier for detection and classification of DGA generated domains. We have evaluated our approach using the dataset released by DMD 2018 Shared Task for both binary classification and multiclass classification scenario. Our methodology of CNN with NB for binary classification has been awarded the first rank in this DMD 2018 shared task. © Springer Nature Singapore Pte Ltd. 2019.

Communications in Computer and Information Science Security in Computing and Communications

Transfer Learning Approach for Identification of Malicious Domain Names

Ever growing World Wide Web results in a large volume of web pages with variety of topics. Many applications such as information filtering and focused crawling demand large scale topic classification of a web page. To classify the web pages, URL based approach is proposed by which downloading the contents of the web page for classification purpose is avoided. In this paper, an automated way of learning category specific universal dictionary of discriminating URL features is proposed. Using this automatically learnt dictionary, the feature vector dimensionality is made independent of training set and it overcomes the difficulty of handling large scale data. For constructing this dictionary, publicly available ODP dataset have been used. The proposed approach was evaluated by applying the automatically learnt URL feature dictionaries on another dataset that contains search results from Google. Through experiments, it is shown that macro-average precision, recall and F1 values of 0.93, 0.85 and 0.88 have been achieved. We have observed that, the difference is not statistically significant when the universal dictionary is applied instead of using dataset-specific term dictionary. © 2018 IEEE.

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

An Effective and Discriminative Feature Learning for URL Based Web Page Classification

Web page classification has become a challenging task due to the exponential growth of the World Wide Web. Uniform Resource Locator (URL)-based web page classification systems play an important role, but high accuracy may not be achievable as URL contains minimal information. Nevertheless, URL-based classifiers along with rejection framework can be used as a first-level filter in a multistage classifier, and a costlier feature extraction from contents may be done in later stages. However, noisy and irrelevant features present in URL demand feature selection methods for URL classification. Therefore, we propose a supervised feature selection method by which relevant URL features are identified using statistical methods. We propose a new feature weighting method for a Naive Bayes classifier by embedding the term goodness obtained from the feature selection method. We also propose a rejection framework to the Naive Bayes classifier by using posterior probability for determining the confidence score. The proposed method is evaluated on the Open Directory Project and WebKB data sets. Experimental results show that our method can be an effective first-level filter. McNemar tests confirm that our approach significantly improves the performance. © 2018 Wiley Periodicals, Inc.

Computational Intelligence

A Naive Bayes approach for URL classification with supervised feature selection and rejection framework

Design of Kids-specific URL Classifier using Recurrent Convolutional Neural Network

Information Processing & Management

Balancing between over-weighting and under-weighting in supervised term weighting

Knowledge-Based Systems

MDLText: An efficient and lightweight text classifier

Proceedings of the Third International Symposium on Women in Computing and Informatics - WCI '15

Identifying Health Domain URLs using SVM

Journal of Computer Science

SUPERVISED TERM WEIGHTING METHODS FOR URL CLASSIFICATION

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

URL-based Web Page Classification - A New Method for URL-based Web Page Classification Using n-Gram Language Models

Experimental Study Of Feature Weighting Techniques For URL Based Webpage Classification

Journal	Data powered by TypesetProcedia Computer Science
Publisher	Data powered by TypesetElsevier BV
ISSN	1877-0509
Open Access	No