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Generating Weighted Fuzzy Rules from Training Instances Using Genetic Algorithms to Handle the Iris Data Classification Problem
並列摘要
In recent years, many researchers have focused on applying the fuzzy set theory to generate fuzzy rules from training instances to deal with the Iris data classification problem. In this paper, we propose a new method to automatically generate weighted fuzzy rules from training instances by using genetic algorithms to handle the Iris data classification problem, where the attributes appearing in the antecedent parts of the generated fuzzy rules have different weights. The proposed method can achieve a higher average classification accuracy rate and generate fewer fuzzy rules than the existing methods.
被引用紀錄
Chou, M. Y. (2007). 利用模糊基因法探討關聯式資料庫之虛值 [master's thesis, Yuan Ze University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0009-3006200712312100
延伸閱讀
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- 陳宥臻（2015）。Reliability Assessment Based on Progressively Type-I Interval Censored Measurements Using Genetic Algorithm and Differential Evolution Methods〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2015.00231
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