In this paper, we systematically study the problem of dataless hierarchical text classification. Unlike standard text classification schemes that rely on supervised training, dataless classification depends on understanding the labels of the sought after categories and requires no labeled data. Given a collection of text documents and a set of labels, we show that understanding the labels can b...

1 Introduction Most data collections from real world are in text format. Those data are considered semi structured data because they have a small organized structure. Modeling and implementing on semi structured data from recent data bases grows continually in the last years. More over, information retrieval applications, as indexing methods of text documents, have been adapted in order to work...

Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated three approaches to build a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a metaclassifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers c...

Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for acc...

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as produc...

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as produc...

Text classification plays an large part in today's world, where the amount of information available is overwhelming. Although most text classification work is related to topical classification, the categorization of more subjective documents that depend more on style and the author's opinion is also important. Websites such as Amazon, IMDB, and Rotten Tomatoes rely on opinions and reviews to ke...

The objective of this thesis is to develop efficient text classification models to classify text documents. In usual text mining algorithms, a document is represented as a vector whose dimension is the number of distinct keywords in it, which can be very large. Consequently, traditional text classification can be computationally expensive. In this work, feature extraction through the non-negati...

Text categorization is the process of classifying documents into a predefined set of categories based on its contents of keywords. Text classification is an extended type of text categorization where the text is further categorized into sub-categories. Many algorithms have been proposed and implemented to solve the problem of English text categorization and classification. However, few studies ...

Automatic text classification is the problem of automatically assigning predefined categories to free text documents, thus allowing for less manual labors required by traditional classification methods. When we apply binary classification to multi-class classification for text classification, we usually use the one-against-the-rest method. In this method, if a document belongs to a particular c...