Feature Selection Using Particle Swarm Optimization in Text Categorization
نویسندگان
چکیده
منابع مشابه
Feature Selection Using Particle Swarm Optimization in Text Categorization
Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on par...
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Text Categorization (TC) has become recently an important technology in the field of organizing a huge number of documents. Feature Selection (FS) is commonly used to reduce dimensionality of text datasets with huge number of features which would be difficult to process further. In this paper we have implemented an efficient feature selection algorithm based on Particle Swarm Optimization (PSO)...
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Feature Selection is a pre-processing step in knowledge discovery from data (KDD) which aims at retrieving relevant data from the database beforehand. It imparts quality to the results of data mining tasks by selecting optimal feature set from larger set of features. Various feature selection techniques have been proposed in past which, unfortunately, suffer from unavoidable problems such as hi...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence and Soft Computing Research
سال: 2015
ISSN: 2083-2567
DOI: 10.1515/jaiscr-2015-0031