Term Extraction Using Hybrid Fuzzy Particle Swarm Optimization
نویسندگان
چکیده
منابع مشابه
Fuzzy Apriori Rule Extraction Using Multi-Objective Particle Swarm Optimization: The Case of Credit Scoring
There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...
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Term extraction is one of the layers in the ontology development process which has the task to extract all the terms contained in the input document automatically. The purpose of this process is to generate list of terms that are relevant to the domain of the input document. In the literature there are many approaches, techniques and algorithms used for term extraction. In this paper we propose...
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There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...
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ژورنال
عنوان ژورنال: Information Technology Journal
سال: 2014
ISSN: 1812-5638
DOI: 10.3923/itj.2014.1631.1639