Text Classification with the Combination of Feature Selection and Machine Learning Algorithm

Text classification refers to determine the class of an unknown text according to its content in the given classification system. In this paper the enhanced features are used to find distribution of a word in a single document or multiple number of documents. It can be exploited by a TF-IDF style equation, and different features are combined using ensemble learning techniques. Features are not enough for fully capturing the information contained in a document. Although these values are useful for text categorization, they have not fully articulated the abundant information contained in the document. Text categorization gives best results especially when distributional features like compactness, first appearance, stop word list are combined together. INTRODUCTION: Data mining refers to extracting or “mining“ knowledge from large amounts of data. A number of different data stores on which mining can be performed. This includes relational databases, data warehouses, World-Wide Web, text databases, multimedia databases and so on. Text databases are databases which contains word descriptions for objects. These word descriptions are usually not simple keywords but rather long sentences or paragraphs, such as product specifications, error or bug reports, warning messages, summary reports, notes, or other documents. Text databases may be highly unstructured (such as Web pages on the World Wide Web). Some text databases may be somewhat structured (such as e-mails messages), While others are relatively well structured (such as library databases). Since content based document management is one of the most important text database in nowadays because the content is in the form of digital form. The widespread and increasing availability of text documents in electronic form increases the importance of using automatic methods to analyze the content of text documents, because the method using domain experts to identify new text documents Allocate them to well-defined categories is timeconsuming and expensive, has limits, and does not provide continuous measure of the degree of