Hierarchical classification for multiple, distributed web databases

The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Urdu News Content Classification Using Machine Learning Algorithms

As the world has become a global village, the flow of news in terms of volume and speed increases. It is necessary to engage computing machines for assisting people in dealing with this massive data. The availability of different types of news and such material on the Internet serves as a source of information for billions of users. Millions of people in our subcontinent speak and understand Urdu. There are several classification techniques that are available and are applied to classify English news like political, Education, Medical, etc. Plenty of research work has been done in multiple languages but Urdu is still to be worked on due to a lack of resources. This research evaluates the performance of twelve (12) different Machine learning classifiers for the Urdu News text Classification problem. The analysis was performed on a relatively big and recent collection of Urdu text that contains over 0.15 million (153,050) labeled instances of eight different classes. In addition, after applying pre-processing techniques, the TF-IDF weighting technique was adopted for feature selection and data extraction. After evaluating various machine learning methods, the SVM outperforms the other eleven algorithms with an accuracy of 91.37 %. We also compare its results with other classifiers like linear SVM, Logistic regression, SGD, Naïve bays, ridge regression, and a few others

Delineating Knowledge Domains in Scientific Domains in Scientific Literature using Machine Learning (ML)

The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like data preparation followed by processing, transformation, and application of classification techniques culminating in the validation of the results

Simple and efficient classification scheme based on specific vocabulary

Assuming a binomial distribution for word occurrence, we propose computing a standardized Z score to define the specific vocabulary of a subset compared to that of the entire corpus. This approach is applied to weight terms (character n-gram, word, stem, lemma or sequence of them) which characterize a document. We then show how these Z score values can be used to derive a simple and efficient categorization scheme. To evaluate this proposition and demonstrate its effectiveness, we develop two experiments. First, the system must categorize speeches given by B. Obama as being either electoral or presidential speech. In a second experiment, sentences are extracted from these speeches and then categorized under the headings electoral or presidential. Based on these evaluations, the proposed classification scheme tends to perform better than a support vector machine model for both experiments, on the one hand, and on the other, shows a better performance level than a Naïve Bayes classifier on the first test and a slightly lower performance on the second (10-fold cross validation

Text Message Categorization of Collaborative Learning Skills in Online Discussion using Neural Network

This paper presents research in neural network approach for text messages categorization of collaborative learning skill in an online discussion. Although a neural network is a popular method for text categorization in the research area of machine learning, unfortunately, the use of neural network in educational settings is rare. Usually, text categorization by neural network is employed to categorize news articles, emails, product reviews, and web pages. In an online discussion, text categorization that is used to classify the message sent by the student into a certain category is often manual, requiring skilled human specialists. However, human categorization is not an effective way for a number of reasons; time- consuming, labor-intensive, lack of consistency in a category, and costly. Therefore, this paper proposes a neural network approach to code the message automatically. Results show that neural networks achieving useful classification on eight categories of collaborative learning skills in an online discussion as measured based on precision, recall, and balanced F-measure

Online Social Network Bullying Detection Using Intelligence Techniques

AbstractSocial networking sites (SNS) is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. However, Social Networking Sites is providing opportunities for cyberbullying activities. Cyberbullying is harassing or insulting a person by sending messages of hurting or threatening nature using electronic communication. Cyberbullying poses significant threat to physical and mental health of the victims.Detection of cyberbullying and the provision of subsequent preventive measures are the main courses of action to combat cyberbullying. The proposed method is an effective method to detect cyberbullying activities on social media. The detection method can identify the presence of cyberbullying terms and classify cyberbullying activities in social network such as Flaming, Harassment, Racism and Terrorism, using Fuzzy logic and Genetic algorithm. The effectiveness of the system is increased using Fuzzy rule set to retrieve relevant data for classification from the input. In the proposed method Genetic algorithm is also used, for optimizing the parameters and to obtain precise output