A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm
المؤلفون المشاركون
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-08-15
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
For data mining, reducing the unnecessary redundant attributes which was known as attribute reduction (AR), in particular, reducts with minimal cardinality, is an important preprocessing step.
In the paper, by a coding method of combination subset of attributes set, a novel search strategy for minimal attribute reduction based on rough set theory (RST) and fish swarm algorithm (FSA) is proposed.
The method identifies the core attributes by discernibility matrix firstly and all the subsets of noncore attribute sets with the same cardinality were encoded into integers as the individuals of FSA.
Then, the evolutionary direction of the individual is limited to a certain extent by the coding method.
The fitness function of an individual is defined based on the attribute dependency of RST, and FSA was used to find the optimal set of reducts.
In each loop, if the maximum attribute dependency and the attribute dependency of condition attribute set are equal, then the algorithm terminates, otherwise adding a single attribute to the next loop.
Some well-known datasets from UCI were selected to verify this method.
The experimental results show that the proposed method searches the minimal attribute reduction set effectively and it has the excellent global search ability.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Su, Yuebin& Guo, Jin. 2017. A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1141051
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Su, Yuebin& Guo, Jin. A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1141051
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Su, Yuebin& Guo, Jin. A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1141051
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1141051
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر