Text Classification using Association Rule with a Hybrid Concept of Naive Bayes Classifier and Genetic Algorithm.
Abstract
Text classification is the automated assignment of natural language texts to
predefined categories based on their content. 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 producing summaries, answering questions or extracting data. Now a
day the demand of text classification is increasing tremendously. Keeping this
demand into consideration, new and updated techniques are being developed for
the purpose of automated text classification. This paper presents a new
algorithm for text classification. Instead of using words, word relation i.e.
association rules is used to derive feature set from pre-classified text
documents. The concept of Naive Bayes Classifier is then used on derived
features and finally a concept of Genetic Algorithm has been added for final
classification. A system based on the proposed algorithm has been implemented
and tested. The experimental results show that the proposed system works as a
successful text classifier.
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